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Using 3D reconstruction techniques within computer vision frameworks can result in more robust and accurate solutions. However, the main challenge lies in the high computation and memory resources required by such methods. To reduce the complexity of these frameworks, a practical solution is to use geometric landmarks instead of the entire image. Therefore, in this paper the problem of 3D shape recovery of a set of standard 2D landmarks, on a single human face image is faced using multi-dimensional scaling (MDS) approach to find a 3D embedding for a set of standard 2D points. Hence, MDS approach is used for the first time in this study to establish an unbiased mapping from 2D landmark space to the corresponding 3D shape space. A deep neural network learns the pairwise 3D dissimilarity among standard 2D landmarks. This scheme leads to find a symmetric dissimilarity matrix, to be fed into the MDS approach to appropriately recovering the 3D shape of corresponding 2D landmarks. In the case of complex input image formations like posedness or perspective projection causing occlusion in the input image, an autoencoder component is used in the proposed framework, as an occlusion removal part, which turns different input views of the human face into a profile view. The results of performance evaluation using variety of synthetic and real-world human face datasets, including NoW dataset, Besel Face Model (BFM), CelebA, CoMA - FLAME, and CASIA-3D, indicates the superiority of the proposed framework, despite its small number of training parameters, with the related state-of-the-art and recent 3D shape recovery of landmark methods from the literature, in terms of unbiasedness, independent projection, efficiency and accuracy. Further, ablation study is performed to find the best training scheme of the deep learning components. All codes and public data of our paper are publicly available for research purposes at https://github.com/s2kamyab/DeepMDS. [ABSTRACT FROM AUTHOR]

Accurate 3D human pose estimation is a challenging task due to occlusion and depth ambiguity. In this paper, we introduce a multi-hop graph transformer network designed for 2D-to-3D human pose estimation in videos by leveraging the strengths of multi-head self-attention and multi-hop graph convolutional networks with disentangled neighborhoods to capture spatio-temporal dependencies and handle long-range interactions. The proposed network architecture consists of a graph attention block composed of stacked layers of multi-head self-attention and graph convolution with learnable adjacency matrix, and a multi-hop graph convolutional block comprised of multi-hop convolutional and dilated convolutional layers. The combination of multi-head self-attention and multi-hop graph convolutional layers enables the model to capture both local and global dependencies, while the integration of dilated convolutional layers enhances the model's ability to handle spatial details required for accurate localization of the human body joints. Extensive experiments demonstrate the effectiveness and generalization ability of our model, achieving competitive performance on benchmark datasets. [ABSTRACT FROM AUTHOR]

Gait recognition is an important biometric technology that allows for the remote collection of stakeholders' characteristics, without requiring their explicit cooperation. It has gained considerable attention in the fields of criminal investigation and intelligent security. Previous studies have shown that local gait features can enhance gait recognition performance by improving robustness to disturbances. However, global gait features also play a crucial role in gait recognition. Many researchers have utilized convolutional operations to extract global features, but these operations tend to focus on features within the receptive field, neglecting those outside of it. Therefore, the potential of global gait features has not been fully explored. In this paper, we propose a gait recognition framework based on vision transformers, aiming to enhance the extraction of global gait features. We introduce an adaptive multi-frame global feature mapping (AMGM) method to address the challenge of inconsistent feature dimensions caused by variations in the number of gait frames when fusing global and local features. We evaluate our model on the latest datasets, and the experimental results demonstrate a significant breakthrough. Notably, our model achieves state-of-the-art recognition accuracy, particularly in scenarios where subjects are wearing coats. Additionally, our model achieves remarkable improvements in recognition accuracy through training with small sample sets. [ABSTRACT FROM AUTHOR]

Seam carving is a method of resizing images based on content awareness. It can realize image retargeting while retaining the main content of the image. However, it may also be maliciously used to tamper with images, such as changing image semantic content by object removal. Therefore, seam carving detection has become important in image forensics. In this paper, a noise residual-based deep learning method is proposed to detect seam carving images. We try to learn the local noise in-consistency of images to recognize them. Firstly, a noise residual extraction segment is used to learn local noise features in the image, and a noise augmentation module is designed to enrich the features, which leverages the noise features extracted from a steganalysis rich model filter to discover the noise in-consistency between authentic and tampered regions. Then, through the feature dimensionality reduction section, the features are further learned and the size of feature maps are reduced. Finally, the output is obtained through global average pooling and a fully-connected layer. A careful testing strategy is further proposed, which greatly improves the detection performance, especially for seam carving with small scaling ratios. The experimental results demonstrate that our method achieves state-of-the-art performance at various scales, and has good robustness and generalization compared with other methods. [ABSTRACT FROM AUTHOR]

Convolutional neural networks have achieved remarkable success in single image dehazing tasks, and previous studies verified the dehazing performance of the U-shaped framework. However, most existing U-shaped architecture dehazing networks still face challenges in sufficiently dealing with a large area of haze with low visibility. In this paper, we propose a novel dehazing network named Double U-Net(DU-Net). Specifically, to reduce the interference of haze features in the encoder to the recovery stage when skip-connecting to the decoder directly, we develop a new architecture firstly, which is composed of an extended encoder–decoder. Besides, the hierarchical depth-wise convolution block(HDCB) is designed to gradually increase the receptive field by leveraging the depth-wise convolution, enriching the global information. Moreover, we propose a multi-branch interactive fusion(MIF) which achieves efficient cross-branch and cross-channel interaction through parallel multiple 1D convolutions. Extensive experiments on both synthetic and real-world hazy images demonstrate the effectiveness of our proposed method. • We construct a Double U-Net for image dehazing, by extending the encoder–decoder. • We build hierarchical convolution blocks to obtain a multi-scale receptive field. • A branch fusion is proposed to enhance cross-branch and cross-channel interaction. [ABSTRACT FROM AUTHOR]

Due to the absorption and scattering of light on the water surface, underwater images often face challenges such as low contrast, color deviation, insufficient exposure, and blurred details, exacerbating the difficulty of underwater tasks. In recent years, underwater image enhancement has become increasingly crucial in marine applications. Among existing underwater enhancement methods, the focus has often been on pixel-level learning, which may lead to image noise and an inability to finely adjust the image. In this paper, we propose a dual-branch Transformer-CNN Parameter Filtering network for underwater image enhancement, referred to as DTCPF. Specifically, to better aggregate window information, we introduce an overlapping window self-attention module to enhance interaction between windows. Additionally, we employ an improved Transformer encoder and decoder, utilizing long-distance attention and reversible neural networks to extract low-frequency and high-frequency information from the image. Moreover, we introduce a regression parameter filtering group for regression prediction, using the predicted parameters to enhance the image and obtain a reliable underwater enhancement model. Our approach undergoes qualitative and quantitative analyses on four real underwater datasets, demonstrating outstanding performance. [ABSTRACT FROM AUTHOR]

Recent self-supervised learning methods, where instance discrimination task is a fundamental way of pretraining convolutional neural networks (CNN), excel in transfer learning performance. Even though instance discrimination task is a well suited pretraining method for classification with its image-level learning, lack of dense representation learning makes it sub-optimal for localization tasks such as object detection. In this paper, we aim to mitigate this shortcoming of instance discrimination task by extending it to jointly learn dense representations alongside image-level representations. We add a segmentation branch parallel to the image-level learning to predict class-agnostic masks, enhancing location-awareness of the representations. We show the effectiveness of our pretraining approach on localization tasks by transferring the learned representations to object detection and segmentation tasks, providing relative improvements by up to 1.7% AP on PASCAL VOC and 0.8% AP on COCO object detection, 0.8% AP on COCO instance segmentation and 3.6% mIoU on PASCAL VOC semantic segmentation respectively. • Convolutional Neural Networks lose localization info with instance discrimination task. • Augmenting instance discrimination with an extra dense task enhances localization. • Unsupervised foreground segmentation boosts localization in (non-)contrastive methods. • Improved localization in SSL enhances transfer learning in dense downstream tasks. [ABSTRACT FROM AUTHOR]

Video anomaly detection aims to identify a set of abnormal events in videos. Deep reconstruction and prediction-based models have been employed to detect anomalies. Deep reconstruction models sometimes recreate the abnormal events along with the normal ones. However, the prediction-based approaches have demonstrated encouraging results. This paper presents a video vision transformer (ViViT) based generative adversarial network (GAN), TransGANomaly, a novel approach for detecting anomalies. The proposed framework is a video frame predictor and trained only on normal video data adversarially. The generator of the GAN is a ViViT network that receives 3D input tokens from the video snippets. The generator aims to predict the future frame based on past sequences. After that, the predicted and original frames are given to the model's discriminator for binary classification. Extensive experiments have been performed on UCSD Pedestrian, CUHK Avenue, and ShanghiaTech datasets to validate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Deep learning methods utilizing Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable success in volumetric medical image analysis. While successful, the symmetrical structure of numerous networks pays insufficient attention to the encoding phase, and the large amount of memory occupied by voxels leads to unnecessary redundancy in the network. In this paper, we present a novel approach to handle volumetric medical images by converting them into point cloud and introduce a new asymmetrical segmentation architecture. We propose a dual-path encoder that fully captures both dense and sparse representations of the input point cloud sampled from volumes. Moreover, the two obtained representations are subtracted at the skip connection as a complementary feature during the decoding stage. Experimental results on the Brain Tumor Segmentation (BraTS) and the Multi-sequence Cardiac MR Segmentation tasks demonstrate the great potential of our point-based method for volumetric medical image segmentation. [ABSTRACT FROM AUTHOR]

The images in foggy weather are often degraded which may affect the object detection process. Hence, in recent times, several schemes have been designed to eliminate the haze effect and enhance the performance of computer vision systems. Although these methods reduce the haze effect, they also produce over-saturation and over-degradation on most occasions. These problems occur due to inadequate utilization of the regional haze properties during the dehazing process. Therefore, a new dehazing model is proposed in this paper to address the issues faced by existing dehazing algorithms. The residual convolutional neural network (RCNN) with 14 layers is developed in the present work. In this model, features to indicate the extent of haze are extracted using the regional characteristics of a hazy image and are used as the fourth channel along with the three colour channels of a hazy image. The proposed RCNN is trained with 4-channel hazy images as input and their corresponding haze-free images as output. The results of our model are obtained with several images taken from three different datasets. Through the experimentation, quantitative and qualitative improvements are observed when compared to state-of-the-art dehazing methods. • The local-haze characteristics driven dehazing method is developed in this work. • The regional haze properties are extracted using pixel-wise local max-min difference and pixel intensities of the hazy image. • A 4-channel hazy image is developed by concatenating the regional haze properties map to the hazy image. • A 14-layered RCNN is developed with the 4-channel hazy image as input and the haze-free image as output. [ABSTRACT FROM AUTHOR]

In this paper, an eye-tracking dataset of movie viewing in the immersive environment is developed, which contains 256 movie clips with 2K QHD resolution and corresponding movie genre labels from IMDb (Internet Movie Database). The dataset provides the audio-visual clues for studying the human visual attention when watching movie using a VR headset, by recording the eye movements using integrated eye tracker. To provide benchmarks for a saliency prediction for movie viewing in the immersive environment, fifteen computational models are evaluated on the dataset, including a newly developed multi-stream audio-visual saliency prediction model based on deep neural networks, named as MSAV. Detailed quantitative and qualitative comparisons and analyses are also provided. The developed dataset and benchmarks could help to facilitate the studies of visual saliency prediction for movie viewing in the immersive environments. [ABSTRACT FROM AUTHOR]

Accurate segmentation of nasopharyngeal carcinoma (NPC) in magnetic resonance (MR) images is crucial for radiotherapy planning. However, vanilla 2D/3D deep convolutional networks fail to gain satisfying NPC segmentation results, since 3D methods suffer from inter-slice discontinuities and 2D methods lack inter-slice context learning. To address this problem, this paper proposes a 2.5D learning Selective Inter-slice Context Network (SICNet) for NPC segmentation. The proposed SICNet comprises a main encoder that extracts target features from the middle slice and an auxiliary encoder that learns inter-slice context from adjacent slices. The features from two encoders are fused by the Inter-slice Context Fusion (ICF) module and then sent to the decoder for predictions. Furthermore, a knowledge distillation module named Mask-Guided Self-knowledge Distillation (MGSD) is proposed to help the auxiliary encoder learn context selectively. Specifically, with the guidance of mask labels, MGSD tells the auxiliary encoder where to look and how to learn, thus achieving selective inter-slice context learning and boosting segmentation accuracy. The proposed SICNet achieves a DSC of 74.38 ± 11.99, an HD95 of 9.31 ± 2.68, and an ASSD of 1.46 ± 0.76 on an in-house NPC dataset, outperforming other state-of-the-art segmentation methods. [ABSTRACT FROM AUTHOR]

Domain generalizable person re-identification (DG ReID) aims to obtain a model that can be applied directly to unseen domains once trained on a set of source domains (datasets collected from different camera networks). Among current DG ReID methods, instance normalization is a promising solution to reduce the effect of domain bias, but it inevitably filters out some discriminative information. Besides, since most pioneering approaches cannot effectively address the loss of discriminative information, this paper proposes a Style Elimination and Information Restitution (SEIR) module and constructs a generalizable framework. Specifically, we utilize instance normalization to reinforce the generalization capability on unseen domains. Then, the instance-specific mean and variance are employed to construct a feature vector that includes discriminative and style information. Besides, an encoder–decoder structure is designed to restitute the discriminative information to ensure a high recognition rate. Finally, a dual optimization strategy is devised to update the proposed module by simulating a real train-test process to enhance the generalization robustness further and prevent SEIR from overfitting to the source domain. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods by a large margin on the public DG ReID benchmarks. • A Style Elimination and Information Restitution module for handling domain bias. • An optimization strategy for improving the generalization ability. • We propose a framework for generalizable person re-identification. • Significant results were achieved for addressing the generalizable person re-identification. [ABSTRACT FROM AUTHOR]

Lossy compression introduces artifacts, and many conventional in-loop filters have been adopted in the AV1 standard to reduce these artifacts. Researchers have explored deep learning-based filters to remove artifacts in the compression loop. However, the high computational complexity of CNN-based filters remains a challenge. In this paper, a Texture- and Motion-Aware Perception (TMAP) in-loop filter is proposed to addresses this issue by selectively applying CNNs to texture-rich and high-motion regions, while utilizing non-learning methods to detect these regions. The proposed method introduces a new CNN structure, the Dense-Dual-Field Network (DDFN), which leverages a larger receptive field to enhance the quality of reconstructed frames by incorporating more contextual information. Furthermore, to improve perceptual quality, a novel loss function integrating wavelet-based perceptual information is presented. Experimental results demonstrate the superiority of our proposed models over other lightweight CNN models, and the effectiveness of the perceptual loss function is validated using the VMAF metric. [ABSTRACT FROM AUTHOR]

In the last decade, the exploration of deep-sea ecosystems has surged, offering exciting prospects for discovering untapped resources such as medical drugs, food and energy sources, and renewable energy products. Consequently, research in underwater image processing has witnessed substantial growth. However, underwater imaging poses significant challenges, particularly without sophisticated, specialized cameras. Traditional cameras are impacted by absorption and scattering in the aquatic environment, producing hazy images with a blue–green tint. This phenomenon holds implications for marine research and other disciplines that rely on underwater imaging. While hardware advancements have been made over the years, image processing remains a valuable, cost-effective, and practical approach for underwater enhancement. Despite the existence of state-of-the-art techniques for underwater enhancement and restoration, their performance is often inconsistent. While some methods excel in contrast restoration, color restoration remains a pervasive challenge. In this paper, we introduce Sea-Pix-GAN, a Generative Adversarial Network (GAN)-based model that addresses these issues in underwater image enhancement. We redefine the problem as an image-to-image translation task and tailor the objective and loss functions to achieve color, content, and style transfer. The model is trained on a large dataset of underwater scenes, encompassing the diverse color dynamics of underwater subjects. Sea-Pix-GAN demonstrates promising results in restoring color, contrast, texture, and saturation. To validate its effectiveness, we compare the performance of Sea-Pix-GAN quantitatively based on metrics like PSNR, SSIM, and UIQM and qualitatively against several existing techniques. [ABSTRACT FROM AUTHOR]

Recent improvements in dynamic hand gesture recognition have seen a shift from traditional convolutional architectures to attention-based networks. These attention networks have been proven to outclass CNN + LSTM architectures, showing higher accuracy as well as reduced model parameters. Especially, skeleton-based attention networks have been shown to outperform visual-based networks due to the rich information from skeleton-based hand features. However, there is an opportunity to introduce complementary features from other modalities like RGB, depth, and optical flow images to enhance the recognition capability of skeleton-based networks. This paper aims to explore the addition of a multimodal fusion network to a skeleton-based Hierarchical Self-Attention Network (MF-HAN) and test for increased model effectiveness. Unlike traditional fusion techniques, this fusion network uses features derived from other sources of multimodal data in a reduced feature space using a cross-attention layer. The model outperforms its root model and other state-of-the-art models on the SHREC'17 track dataset, especially in the 28 gestures setting by more than 1 % in gesture classification accuracy. The experimentation was tested on the DHG dataset as well. [ABSTRACT FROM AUTHOR]

In social networks, people usually communicate online by posting and passing images, people will find that there are many similar or even visually identical images. When the images determine whether the published information is true or false, original images became crucial, and at the same time, deep learning excelled in image identification. Combining the original image identification and deep learning methods, this paper proposes the original image identification method based on the deep network models, aiming to use the deep network models to identify the original images between the original images and a bunch of variant images of the original images, and obtain satisfactory results. In this way, through the trained network models, the original images can be identified and can help the viewers to determine the correctness of the information. Experimental results verify the feasibility, effectiveness and security of the proposed method. • The method can prevent the images from being stolen during transmission. • The method can achieve good identification results on the image databases. • The method is feasible and flexible in actual operation. [ABSTRACT FROM AUTHOR]

• An image fusion method is proposed that can effectively highlight infrared target features and retain visible visual information. • Infrared targets are analyzed based on the saliency weight map. • The target contour and background texture are refined by a method based on weight refinement of guided filtering. • A multi-layer fusion strategy is used to fuse saliency weight maps at multiple scales. Image fusion is the fusion of multiple images from the same scene to produce a more informative image, and infrared and visible image fusion is an important branch of image fusion. To tackle the issues of diminished luminosity in the infrared target, inconspicuous target features, and blurred texture of the fused image after the fusion of infrared and visible images. This paper introduces a novel effective fusion framework that merges multi-scale Convolutional Neural Networks (CNN) with saliency weight maps. First, the method measures the source image features to estimate the initial saliency weight map. Then, the initial weight map is segmented and optimized using a guided filter before being further processed by CNN. Next, a trained Siamese convolutional network is used to solve the two key problems of activity measure and weight assignment. Meanwhile, a multi-layer fusion strategy is designed to effectively retain the luminance of the infrared target and the texture information in the visible background. Finally, adaptive adjustment of the fusion coefficients is achieved by employing saliency. The experimental results show that the method outperforms the state-of-the-art algorithms in terms of both subjective visual quality and objective evaluation effects. [ABSTRACT FROM AUTHOR]

• A novel attention module is introduced to capture important action features in a video both at pix level and channel level. • Another novel attention module is proposed to highlight discriminative convolutional features at channel level. • Based on the above two attention modules, a new network architecture is proposed for action classification in videos. Human action recognition in videos is a fundamental and important topic in computer vision, and modeling spatial–temporal dynamics in a video is crucial for action classification. In this paper, a novel attention module named Channel-wise Non-local Attention Module (CNAM) is proposed to highlight the important features both spatially and temporally. Besides, another new attention module named Channel-wise Attention Recalibration Module (CARM) is developed to focus on capturing discriminative features at channel level. Based on these two attention modules, a novel convolutional neural network named Residual Attention Fusion Network (RAFN) is proposed to model long-range temporal structure and capture more discriminative action features at the same time. More specifically, first, a sparse temporal sampling strategy is adopted to uniformly sample video data as input to RAFN along the temporal dimension. Secondly, the attention modules CNAM and CARM are plugged into residual network for highlighting important action regions around actors. Finally, the classification scores of four streams of RAFN are combined by late fusion. The experimental results on HMDB51 and UCF101 demonstrate the effectiveness and excellent recognition performance of our proposed method. [ABSTRACT FROM AUTHOR]

Highlights • We propose a novel framework to person re-identification. • We combine Gaussian features with deep semantic features to enhance the discrimination of overall features. • Two identification losses and a verification loss are used to our fusion model. • Experimental results on benchmarks achieve the state-of-the-art performance of person re-identification. Abstract Person re-identification is an emerging research field in computer vision. Our paper aims to study how to improve the discrimination of person features. We find that some peculiarities of people have not been better attention in the semantic features of deep learning. However, some features obtained by traditional methods can better express the color, and these features are an important clue for re-identification. Therefore, in this paper, we combine traditional Gaussian features with deep semantic features to enhance the discrimination of overall features. At last, we have achieved good performance on two public datasets (Market1501 and VIPeR) in three main distance method learning (DML). In addition, we applied this model to the task of vehicle re-identification. Experiments show that our method has a great improvement on the VeRi vehicle dataset. We compare the results with the current high level results, which indicates the effectiveness of our model. [ABSTRACT FROM AUTHOR]

Highlights • Our method can model various temporal dynamics embedded in videos. • The representations of our method are endowed with discriminative ability. • The dimension of the learned representation is as same as the original one. The aim of this paper is to model long-term evolution of an action video with temporal multi-scale representation. This task is tough due to huge intra-class variations in motion speed. Most of the existing methods consider evolution modeling and multi-scale feature fusion in two separated phases, which generates sub-optimal representation. To address this issue, this paper proposes a novel method to integrate the evolution modeling and multi-scale representation into a unified framework. The core idea is to introduce a temporal multi-scale smoothing vector, which is used to define how the representations at different temporal scales are combined together for frame smoothing. By formulating the smoothing vector learning, evolution modeling and classifier training jointly, our method can learn a discriminative and flexible representation of multi-scale rather than a single scale or a fixed multi-scale smoothing. Experimental results on three datasets demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Previous deep learning-based single image dehazing methods are implemented by using real-valued convolutional neural network (RV-CNN), which ignore the phase information of the image, and tend to cause models to perform well on synthetic datasets but low generalization on real-world datasets. In this paper, we propose a novel Complex-Valued convolutional Dehazing Network (CVD-Net), which considers both amplitude and phase information of image for haze removal in real-world scenes. Specifically, we construct complex-valued transformation module, including complex-valued convolutional layer and residual block embedded into the middle of the network to extract features of image efficiently, and also design a complex-valued selected fusion module (CVSF) and complex-valued attention module (CVAM) to promote the interaction between different scale features for preserving image detailed information. Both qualitative and quantitative experimental results show that the proposed CVD-Net can effectively remove the haze, and has good generalization in real-world hazy images. [ABSTRACT FROM AUTHOR]

To overcome challenges in multiple-object tracking (MOT) tasks, recent algorithms use interaction cues alongside motion and appearance features. These algorithms use graph neural networks or transformers to extract interaction features that lead to high computation costs. In this paper, a novel interaction cue based on geometric features is presented aiming to detect occlusion and reidentify lost targets with low computational costs. Moreover, in the majority of algorithms, camera motion is considered negligible, which is a strong assumption that is not always true and can lead to identity (ID) switching or mismatching of targets. In this paper, a method for measuring camera motion is presented that efficiently reduces its effect on tracking. The proposed algorithm is evaluated on MOT17 and MOT20 datasets and achieves state-of-the-art performance on MOT17 with comparable results on MOT20. The code is also publicly available. 1 1 https://github.com/mhnasseri/for_tracking. • Cascade association based on the detection score. • A new similarity metric that considers detection's size. • A novel interaction model for occlusion handling and target re-identification. • A novel algorithm for estimating camera motion and removing it. • State-of-the-art results on MOT17 and comparable results on MOT20. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust duplicated-diffusion watermarking model based on deep network (RDD-net). RDD-net employs the encoder-noise-decoder structure for end-to-end training to obtain high image watermarking invisibility and robustness. Firstly, a duplicated-diffusion strategy is employed in the encoder to replicate the original watermark iteratively until all copies are diffused to the whole image so that the generalized ability in resisting various noises is obtained. Then, a channel connection technique is designed to extract inherent image features for fusing watermark for robustness by mining correlations of RGB three channels of the color image. Meanwhile, each channel is fused with watermark to increase robustness. Another attempt to improve the watermarking performance is the optimizer, which optimizes the watermark distribution by evaluating the similarities between the encoded image and the original image, as well as between the encoded image and the noised image. Our extensive experimental results demonstrate that the proposed RDD-net not only resists different noises, but also obtains better image quality and higher robustness than the existing watermarking models. [ABSTRACT FROM AUTHOR]

Person Re-Identification (PR-Id) encounters misclassification issues when re-identifying persons with different backpacks. These bags manifest as large and distinct regions in the images surpassing other finer details of a person. As a result, a CNN model swiftly detects and prioritizes these image regions for re-identification. However, the bags are subject to alterations or may be similar among multiple persons, resulting in misclassification. To ensure that a CNN model does not consider bags as unique features of specific persons and prioritize them for re-identification, images of persons with diverse backpacks are crucial in the training dataset. Moreover, these images enhance the model's focus on other unique regions of a person. Although the current datasets show potential, incorporating such images could enhance their effectiveness. Therefore, in this paper, we propose an indoor PR-Id dataset named "With Bag/Without Bag-ReID" (WB/WoB-ReID). The set "with_bag" in WB/WoB-ReID dataset includes identities with different backpacks for the first time. We also incorporate identities without bags and with varying numbers of image counts in three other sets, namely "without_bag", "both_small," and "both_large". We assess WB/WoB-ReID and three other PR-Id datasets: Market1501, CUHK03, and DukeMTMC-reID on various existing approaches. The highest mAP achieved on the"with_bag" is 74%, "without_bag" is 96.7% and other datasets are 97.78%, 95.20% and 92.4%. The results show that incorporating identities with diverse bags reduces the mAP, highlighting the misclassifications that arise specifically in the presence of bags. [ABSTRACT FROM AUTHOR]

Despite the remarkable results achieved by deep convolutional neural networks (CNNs) in single image super-resolution (SISR), their computational cost increase exponentially as CNN models get deeper and wider. Consequently, depthwise separable convolutions (DSConvs) have emerged as the fundamental building basis for various contemporary lightweight network architectures. However, depthwise convolution is sub-optimal for restoring missing high-frequency details. In this paper, we commence with the vanilla depthwise convolution and progressively develop it to alleviate the above problem. Specifically, we introduce an effective method, kernel attention injection, to integrate intra-kernel correlation into the depthwise convolution layer by incorporating the learned kernel attention into the depthwise kernel. We find that the determinant is capable of capturing the correlation between diagonal elements of the depthwise kernel by summing the products of the diagonals. Therefore, we utilize the determinant as the kernel pooling method. Furthermore, we propose to remove the non-linear activation function behind the depthwise convolution (i.e. , linear depthwise convolution) to preserve informative features for reconstructing faithful high-resolution (HR) images. Built on this recipe, we introduce kernel-attentive linear depthwise separable convolutions (KDSConvs), which exhibits promising super-resolution performance. Our experimental results underline the potential of depthwise convolution in super-resolution tasks by demonstrating that the proposed KDSConvs significantly outperforms DSConvs in terms of both quantitative measurements and visual quality. The code will be released at https://github.com/AwesomeHwang/KALDN. • We propose kernel attention injection to integrate intra-kernel correlation. • We utilize the determinant as the kernel pooling method. • We introduce kernel-attentive linear depthwise separable convolutions. [ABSTRACT FROM AUTHOR]

Feature pyramid network (FPN) is a typical detector commonly for solving the issue of object detection at different scales. However, the lateral connections in FPN lead to the loss of feature information due to the reduction of feature channels. Moreover, the top-down feature fusion will weaken the feature representation in the process of feature delivery because of features with different semantic information. In this paper, we propose a feature pyramid network with channel and content adaptive feature enhancement module (CCA-FPN), which uses a channel adaptive guided mechanism module (CAGM) and multi-scale content adaptive feature enhancement module (MCAFEM) to alleviate these problems. We conduct comprehensive experiments on the MS COCO dataset. By replacing FPN with CCA-FPN in ATSS, our models achieve 1.3 percentage points higher Average Precision (AP) when using ResNet50 as backbone. Furthermore, our CCA-FPN achieves 0.3 percentage points higher than the AugFPN which is the state-of-the-art FPN-based detector. • Targets of different scales often use feature pyramid networks for hierarchical detection. • Using deep learning for object detection, feature enhancement is beneficial for improving detection performance. • When performing feature fusion on the feature layer, it is necessary to reduce the channel dimension. • The reduction of the channel dimension can use the attention mechanism to mine the correlation between channels for compression. • In the learning process of neural networks, the learning target features are adapted using different weight sizes. [ABSTRACT FROM AUTHOR]

Over the past few years, skeleton-based action recognition has attracted great success because the skeleton data is immune to illumination variation, view-point variation, background clutter, scaling, and camera motion. However, effective modeling of the latent information of skeleton data is still a challenging problem. Therefore, in this paper, we propose a novel idea of action embedding with a self-attention Transformer network for skeleton-based action recognition. Our proposed technology mainly comprises of two modules as, (i) action embedding and (ii) self-attention Transformer. The action embedding encodes the relationship between corresponding body joints (e.g., joints of both hands move together for performing clapping action) and thus captures the spatial features of joints. Meanwhile, temporal features and dependencies of body joints are modeled using Transformer architecture. Our method works in a single-stream (end-to-end) fashion, where multiple-layer perceptron (MLP) is used for classification. We carry out an ablation study and evaluate the performance of our model on a small-scale SYSU-3D dataset and large-scale NTU-RGB+D and NTU-RGB+D 120 datasets where the results establish that our method performs better than other state-of-the-art architectures. • Action recognition has several computer applications such as surveillance, content analysis, healthcare, etc. • We have proposed a new action embedding scheme using a graph convolution and link prediction method that can help exploit the relation between distant joints. • We tune the embeddings using a self-attention transformer to preserve longer spatial–temporal dependencies across frames for final multiclass classification. • We have evaluated our method of different public datasets, including SYSU-3D, NTU RGB+D, and NTU RGB+D 120, where it surpasses state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In the field of face anti-spoofing (FAS), how to extract the representative features to distinguish between real and spoof faces and train the corresponding deep networks are two vital issues. In this paper, we propose a simple but effective end-to-end FAS model based on an innovative texture extractor and a depth auxiliary supervision mechanism. In the feature extraction stage, we first design the residual gradient convolutions based on the redesigned gradient operators, which are used to extract fine-grained texture features. The extraction of texture features is based on multiple scales by dividing the texture differences between living and spoofing faces into three levels reasonably. Then we construct a multiscale residual gradient attention (MRGA) to obtain representative texture features from multiple levels texture features. By combining the proposed feature extractor MRGA and existing vision transformer (ViT), the MRGA-ViT is proposed to generate related semantics and obtain final classification results. In the training stage, we also propose a local depth auxiliary supervision based on a novel adjacent depth loss, which utilizes the correlation information of adjacent pixels adequately compared with traditional depth loss. The proposed MRGA-ViT model achieves competitive performance in generalization and stability ability, e.g., the ACER(%) values of intra testing on OULU-NPU database are 1.8, 2.6, 1.6 ± 1.2 and 1.9 ± 2.7 respectively, the AUC(%) of cross type testing attains 99.45 ± 0.57, the ACER(%) values of cross dataset testing are 28.1 and 36.7 respectively. Experimental results prove that the proposed model is competitive to other state-of-the-art works on generalization and stability performance. • The multi-scale feature extraction based on the multiple levels texture differences. • The gap elimination of representation between local textures and global semantics. • The adjacent depth loss and local depth supervision are effective and generalizable. • The FAS method is stable and generalizable to detect the spoofing face. [ABSTRACT FROM AUTHOR]

Although the deep CNN-based super-resolution methods have achieved outstanding performance, their memory cost and computational complexity severely limit their practical employment. Knowledge distillation (KD), which can efficiently transfer knowledge from a cumbersome network (teacher) to a compact network (student), has demonstrated its advantages in some computer vision applications. The representation of knowledge is vital for knowledge transferring and student learning, which is generally defined in hand-crafted manners or uses the intermediate features directly. In this paper, we propose a model-agnostic meta knowledge distillation method under the teacher–student architecture for the single image super-resolution task. It provides a more flexible and accurate way to help teachers transmit knowledge in accordance with the abilities of students via knowledge representation networks (KRNets) with learnable parameters. Specifically, the texture-aware dynamic kernels are generated from local information to decompose the distillation problem into texture-wise supervision for further promoting the recovery quality of high-frequency details. In addition, the KRNets are optimized in a meta-learning manner to ensure the knowledge transferring and the student learning are beneficial to improving the reconstructed quality of the student. Experiments conducted on various single image super-resolution datasets demonstrate that our proposed method outperforms existing defined knowledge representation-related distillation methods and can help super-resolution algorithms achieve better reconstruction quality without introducing any extra inference complexity. [ABSTRACT FROM AUTHOR]

Although convolutional neural network-based methods have achieved significant performance improvement for Single Image Super-Resolution (SISR), their vast computational cost hinders real-world environment application. Thus, the interest in light networks for SISR is rising. Since existing SISR light models mainly focus on extracting fine local features using convolution operation, they have a limitation in that networks hardly capture global information. To capture the long-range dependency, Non-Local (NL) attention and Transformers have been explored in the SISR task. However, they are still suffering from a balancing problem between performance and computational cost. In this paper, we propose Fast Non-Local attention NETwork (FNLNET) for a super light SISR, which can capture the global representation. To acquire global information, we propose The Fast Non-Local Attention (FNLA) module that has low computational complexity while capturing global representation that reflects long-distance relationships between patches. Then, FNLA requires only 16 times lower computational cost than conventional NL networks while improving performance. In addition, we propose a powerful module called Global Self-Intension Mining (GSIM) that fuses the multi-information resources such as local, and global representation. Our FNLNET shows outstanding performance with fewer parameters and computational costs in the experiments on the benchmark datasets against state-of-the-art light SISR models. [Display omitted] • We propose a novel approach to Non-Local attention in SISR. • We propose a powerful module for SISR that designs multi-branch architecture. • The proposed method demonstrates notable improvements, despite having fewer parameters and MAC. [ABSTRACT FROM AUTHOR]

In this paper, a semi supervised method for classification of satellite images based on Genetic Algorithm (GA) and Radial Basis Function Neural Network (RBFNN) is proposed. Satellite image classification problem has two major concerns to be addressed. The first issue is mixed pixel problem and the second issue is handling large amount of data present in these images. RBFNN function is an efficient network with a large set of tunable parameters. This network is able to generalize the results and is immune to noise. A RBFNN has learning ability and can appropriately react to unseen data. This makes the network a good choice for satellite images. The efficiency of RBFNN is greatly influenced by the learning algorithm and seed point selection. Therefore, in this paper spectral indices are used for seed selection and GA is used to train the network. The proposed method is used to classify the Landsat 8 OLI images of Dongting Lake in South China. The application of this method is shown for detection of flooded area over this region. The performance of the proposed method was analyzed and compared with three existing methods and the error matrix was computed to test the performance of the method. The method yields high producer’s accuracy, consumer’s accuracy and kappa coefficient value which indicated that the proposed classifier is highly effective and efficient. [ABSTRACT FROM AUTHOR]

In contrast to Convolutional Neural Networks (CNNs), Vision Transformers (ViT) cannot capture sequence ordering of input tokens and require position embeddings. As a learnable fixed-dimension vector, the position embedding improves accuracy while limiting the migration of the model between different input sizes. Hence, this paper conducts an empirical study on position embeddings of pre-trained models, which mainly focuses on two questions: (1) What do the position embeddings learn from training? (2) How do the position embeddings affect the self-attention modules? This paper analyzes the pattern of position embedding in pre-trained models and finds that the linear combination of Gabor filters and edge markers can fit the learned position embeddings well. The Gabor filters and edge markers can occupy some channels to append the position information, and the edge markers have flowed to values in self-attention modules. The experimental results can guide future work to choose suitable position embeddings. • Position Embeddings are the linear combination of Gabor Filters and Edge Markers. • The main part of the positional embeddings is input-independent. • Relative position embeddings requires adding edge markers to the value matrix. [ABSTRACT FROM AUTHOR]

Dynamic hand gesture recognition is still an interesting topic for the computer vision community. A set of feature vectors can represent any hand gesture. A Recurrent Neural Network (RNN) can recognize these feature vectors as a hand gesture that analyzes the temporal and contextual information of the gesture sequence. Thus, we proposed a hybrid deep learning framework to recognize dynamic hand gestures. In the Hybrid model GoogleNet is pipelined with a Bidirectional GRU unit to recognize the dynamic hand gesture. Dynamic hand gestures consist of many frames, and features of each frame need to be extracted to get the temporal and dynamic information of the performed gesture. As RNN takes input as a sequence of feature vectors, we extract features from videos using pretrained GoogleNet. As Gated Recurrent Unit is one of the variants of RNN to classify the sequential data, we created a feature vector that corresponds to each video and passed it to the bidirectional GRU (BGRU) network to classify the gestures. We evaluate our model on four publicly available hand gesture datasets. The proposed method performs well and is comparable with the existing methods. For instance, we achieved 98.6% accuracy on Northwestern University Hand Gesture(NWUHG), 99.6% on SKIG, 99.4% on Cambridge Hand Gesture (CHG) datasets respectively. We performed our experiments on DHG14/28 dataset and achieved an accuracy of 97.8% with 14-gesture classes and 92.1% on 28-gesture classes. DHG14/28 dataset contains skeleton and depth data, and our proposed model used depth data and achieved comparable accuracy. • The paper presented a two-stream hybrid model that uses a convolutional neural network to extract the frame-level features and used a BGRU to classify the dynamic hand gesture. • A deeply coupled hybrid pipeline is used to extract spatial and temporal features. • The paper presented a comprehensive review of the BLSTM and BGRU model with different batch sizes. • Experimental results shows that the BGRU model with optical flow and RGB videos performs better than state-of-the-art. [ABSTRACT FROM AUTHOR]

Image quality assessment (IQA) is an indispensable technique in computer vision, which is widely applied in image classification, image clustering. With the development of deep learning, deep neural network (DNN)-based methods have shown impressive performance. Thus, in this paper, we propose a novel method for mechanical equipment fault diagnosis based on IQA. More specifically, we first conduct data acquisition base on our practice. Afterwards, we leverage image processing method for removing noise. Subsequently, we leverage CNN-based method for image classification. Finally, different mechanical equipment images will be grouped into different categories and fault detection can be achieved. Extensive experiments demonstrate the effectiveness and robustness of our method. [ABSTRACT FROM AUTHOR]

• A new method for non-blind correlated noise removal. • Convolutional neural network based. • Fully automatic once the autocovariance function of the noise and the noisy image are given. • Performs better than several methods which in addition may require careful parameter tuning. In this paper, removal of additive, signal-independent, correlated noise from images is considered. We consider the non-blind case, meaning that the stationary autocovariance function of the noise is assumed to be known. The denoising method is based on unrolled optimization where in the half-quadratic energy minimization each proximal step is replaced by a learnt convolutional neural network. The proximal steps take place in learnt transform domains. Functions producing the regularization parameters are also learnt. We assume that we have a distribution for autocovariance functions in order to be able to draw samples. For simplicity, we assume that the noise has low-pass spectral character and a typical autocovariance function has a relatively simple form. The experimental results demonstrate that in terms of PSNR values, the method performs better than two classical methods and a method based on a learnt patch prior. [ABSTRACT FROM AUTHOR]

This paper introduces a novel video saliency model for salient object detection in videos. Firstly, we generate multi-level deep features via a symmetrical convolutional neural network, in which the inputs are the current frame and the optical flow image. Then, the multi-level deep features are integrated in a hierarchical manner using a fusion network, which deploys attention module to make a selection for deep features. Lastly, the integrated deep feature is combined with the boundary information originated from shallow layer of the feature extraction networks, and the saliency map is generated by the saliency prediction step. The key advantages of our model lie on the attention module, the hierarchical integration and the boundary information, in which the former one acts as weight filter and is used to select the most salient regions in deep features, the middle one gives an effective integration manner for features from different layers and the last one provides well-defined boundaries for saliency map. Extensive experiments are performed on two challenging video dataset, and the experimental results show that our model consistently outperforms the state-of-the-art saliency models in a large margin. [ABSTRACT FROM AUTHOR]

• A novel top-down framework is presented to address multi-person pose estimation. • We improved the faster R-CNN detector by infusing the soft-NMS and ASDN items. • Heatmaps instead of activation map are used to pinpoint the precise joints location. Human motion recognition based on computer vision plays an important role in many fields, such as video surveillance, virtual reality, and medical care. To solve the inaccurate multi-person pose estimation problem and improve the generalizability of the extracted features, this paper proposes a multi-person pose estimation method based on a deep convolutional neural network. This method mainly relies on a top-down structure which includes two stages. In the first stage, the bounding boxes that are likely to contain people are first detected by an improved faster R-CNN. Individuals in the complex scenario are then tailored by box cropping. In the second stage, we combine heatmap detection with coordinate regression to address the single person pose estimation problem. Specially, a deep convolutional ResNet is employed to produce heatmaps of human body. The precise location of each joint is achieved by the fully connected conditional random field. Experimental results demonstrate our method achieves comparable performance with the state-of-the-art ones. [ABSTRACT FROM AUTHOR]

Deep learning has been widely applied in image processing and computer vision due to its powerful learning capability. Although some learning models have been proposed to suppress noise in images, most of them are developed for Gaussian noise and few are for impulse noise. This paper proposes an image recovery method based on deep convolutional neural networks for impulse noise removal. The proposed framework falls into two components: a classifier network which divides image pixels into noisy and noise-free, and a regression network which is trained for image reconstruction. In the regression network, the noise-free pixels identified by the classifier network together with the original noisy image are used for recovery of the noisy image. Furthermore, batch normalization is embedded to the network to improve denoising performance. Experimental results show that the proposed method can excellently remove impulse noise, providing clear performance improvements over other state-of-the-art denoising methods. [ABSTRACT FROM AUTHOR]

• We present a novel discriminative model to learn similarity measurement between source domain and target domain. • We freeze part of the network layers to extract well-defined representations from source domain to target domain. • We use auxiliary images labelled by perspective-class to improve categorization performance. Fine-grained categorization is challenging due to its small inter-class and large intra-class variance. Moreover, requiring domain expertise makes fine-grained labelled data much more expensive to acquire. Existing models predominantly require extra information such as bounding box and part annotation in addition to the image category labels, which involves heavy manual labor. In this paper, we propose a novel hierarchical deep transfer learning model, based on a compression convolutional neural network. Our model transfers the learned image representation from large-scale labelled fine-grained datasets to micro fine-grained datasets, which avoids using expensive annotations and realizes visual categorization task effectively. Firstly, we introduce a cohesion domain to measure correlation degree between source domain and target domain. Secondly, the source-domain convolutional neural network is adjusted according to its metrical feedback, in order to select task-specific features that are suitable for transferring to the target domain. Finally, we make most of perspective-class labels, which are inherent attributes of fine-grained data for multi-task learning and learn all the attributes through joint learning to extract more discriminative representations. The proposed model not only economizes training time effectively and achieves high categorization accuracy, but also verifies that the inter-domain feature transition can accelerate learning and optimization. [ABSTRACT FROM AUTHOR]

• A novel approach to gender classification with state-of-the-art accuracy. • Combination between local and contextual features extracted from face images. • Employing deep learning and convolutional neural networks (CNNs). • Easy detection of single or multiple face regions. • A challenging dataset of 42,592 face images for research purposes with annotations. Gender classification aims at recognizing a person's gender. Despite the high accuracy achieved by state-of-the-art methods for this task, there is still room for improvement in generalized and unrestricted datasets. In this paper, we advocate a new strategy inspired by the behavior of humans in gender recognition. Instead of dealing with the face image as a sole feature, we rely on the combination of isolated facial components and a contextual feature which we call the foggy face. Then, we use these features to train deep convolutional neural networks followed by an AdaBoost-based score fusion to infer the final gender class. We evaluate our method on four challenging datasets to demonstrate its efficacy in achieving better or on-par accuracy with state-of-the-art methods. In addition, we present a new face dataset that intensifies the challenges of occluded faces and illumination changes, which we believe to be a much-needed resource for gender classification research. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is the interesting research area that enables us to recognize the expression of the human face in the day-to-day life. Most of the traditional methods fail to recognize the expressions accurately as the expressions are based on the movements of the parts in the human face. The paper proposes the effective method of FER using the proposed Whale- Grasshopper Optimization algorithm based Multi-Support Vector Neural Network (W-GOA-based MultiSVNN). The features from the facial image is extracted using the Scale-Invariant Feature Transform (SIFT) and the proposed Scatter Local Directional Pattern (SLDP). The extracted features are classified using the proposed classifier to recognize the expression of the face. The proposed method of facial recognition enhances the recognition accuracy. The experimentation of the proposed algorithm is performed using the databases, such as Cohn-Kanade AU-Coded Expression Database and The Japanese Female Facial Expression (JAFFE) Database. The proposed algorithm outperforms the existing methods in terms of the accuracy, TPR, and FPR and the values are found to be 0.96, 0.96, and 0.009, respectively. [ABSTRACT FROM AUTHOR]

• Radial feature descriptors improve cell classification and image recommendation. • Textural variations along cellular extra-nuclear narrow band describe cytoplasm. • Hybrid method contains unsupervised and supervised real-time machine learning. • Nuclei segmentation based on hierarchical clustering handles cell clumps and overlap. • Random Forest for classification and CNN for recommendation improve cell analytics. This paper introduces computational tools for cell classification into normal and abnormal, as well as content-based-image-retrieval (CBIR) for cell recommendation. It also proposes the radial feature descriptors (RFD), which define evenly interspaced segments around the nucleus, and proportional to the convexity of the nuclear boundary. Experiments consider Herlev and CRIC image databases as input to classification via Random Forest and bootstrap; we compare 14 different feature sets by means of False Negative Rate (FNR) and Kappa (κ), obtaining FNR = 0.02 and κ = 0.89 for Herlev, and FNR = 0.14 and κ = 0.78 for CRIC. Next, we sort and rank cell images using convolutional neural networks and evaluate performance with the Mean Average Precision (MAP), achieving MAP = 0.84 and MAP = 0.82 for Herlev and CRIC, respectively. Cell classification show encouraging results regarding RFD, including its sensitivity to intensity variation around the nuclear membrane as it bypasses cytoplasm segmentation. [ABSTRACT FROM AUTHOR]

• Well-defined patches can sufficiently classify facial expressions and reduce computation cost. • Well-defined patches have better interference reduction than cropped face. • The proposed network can extract robust features under fast speed and cope with small scale dataset. • The proposed loss can adjust training by network output accordingly and boost up FER performance. One of the key challenge issues of deep-learning-based facial expression recognition (FER) is learning effective and robust features from variant samples. In this paper, Region-based Convolutional Fusion Network (RCFN) is proposed to solve this issue via three aspects. Firstly, a muscle movement model is built to segment out crucial regions of frontal face, providing well-unified patches with benefits of removing unrepresentative regions and greatly reducing interference caused by facial organs with varied sizes and positions among individuals. Secondly, a fast and practical network is constructed to extract robust triple-level features from low level to semantic level in each crucial region and fuse them for FER. Thirdly, constrained punitive loss is introduced to leverage the network training for boosting up FER performance. The experiment results show that RCFN is effective in commonly used datasets like KDEF, CK+, and Oulu-CASIA, and can achieve comparable performance with other state-of-the-art FER methods. [ABSTRACT FROM AUTHOR]

Illustration of the VST-Net where the channel number of the last Conv layer at SubNet1 and SubNet2 is 1. "C", "B" and "R" stands for the Conv, BN and ReLU layers, respectively. • A novel Poisson denoising network based on convolutional neural network is proposed. • The proposed VST-Net inherits and extends the structure and strengths of the traditional VST. • Two-stage learning strategy and joint learning strategy are exploited to investigate the network. Poisson noise occurs in various applications including medical imaging and night vision. There exist many traditional Poisson denoising algorithms, particularly, one class of impressive algorithm is based on variance-stabilizing transformation (VST). Inspired by the traditional VST scheme, in this paper we propose a novel Poisson denoising model based on convolutional neural network, called variance-stabilizing transform network (VST-Net). VST-Net inherits the structures and strengths of the traditional VST scheme via optimizing the nonlinear transformation by means of network design and supervised learning. The whole VST-Net network contains three sub-networks. The first and third sub-networks simulate the forward and inverse Anscombe transforms, respectively. Meanwhile, the second sub-network is devoted to playing the role of approximate Gaussian denoising. Joint learning strategy and two-stage progressive learning strategy are exploited to investigate the rationality and strength of the VST scheme. Experimental results verify the great potential of the VST-driven network. Code is available at: https://github.com/yqx7150/VST-Net. [ABSTRACT FROM AUTHOR]

• We propose a depth-aware saliency model which consists of three networks. • Color saliency network, depth saliency network and saliency fusion network are jointly trained. • The depth saliency network exploits weight initialization to learn effective depth features. • The saliency fusion network can effectively integrate depth saliency map with color saliency map. • Results on five public datasets demonstrate the better performance of our model. This paper proposes a new end-to-end depth-aware saliency model using three convolutional neural networks including color saliency network, depth saliency network and saliency fusion network, for saliency detection in RGBD images and stereoscopic images. Firstly, the color image is fed to the color saliency network to generate the color saliency map. Then, by sharing the weights of some layers in the color saliency network, the depth saliency network exploits the weight initialization and multi-layer pyramid feature fusion to learn effective depth features from the three-channel depth image, which is converted from the original depth map, and generates the depth saliency map. Finally, the saliency fusion network integrates the color saliency map with the depth saliency map into the final saliency map, which can highlight salient object regions and suppress background regions more effectively. Experimental results on five public datasets demonstrate that our model achieves the better performance compared with the state-of-the-art depth-aware saliency models. [ABSTRACT FROM AUTHOR]

• A carrier-less text steganography based on CNN-LSTM network is proposed. • SSH scheme based on beam search can be applied in model sharing situation. • HH scheme for model-free extraction with hash function is proposed. • The tradeoff between text quality and embedded capacity can be balanced. • The proposed schemes achieve high text quality, invisibility, and security. It is a challenge to conduct natural language steganography on Online interactive platforms such as photo-sharing websites since the stego texts should be consistent with the content of the images. In this paper, a novel natural language steganographic framework based on an end-to-end generative network is proposed. A Convolution Neural Network (CNN) combined with Long Short-Term Memory (LSTM) is trained to generate stego descriptions. Word by Word Hiding (WWH) and Sentence by Sentence Hiding (SSH) schemes are proposed to achieve various embedding capacity under the premise of sharing model between the sender and the receiver. Furthermore, a blind extraction scheme called Hash Hiding (HH) is proposed in case that the model is unavailable for data extraction. Comparative experiments show the superiority of the proposed framework. It is verified that the proposed framework is an effective carrier-less steganographic framework with competitive embedding capacity, considerable text quality, and good reversibility. [ABSTRACT FROM AUTHOR]

• A new distance metric method is proposed to improve the retrieval performance. • We build a deep framework to extract advanced image features for large-scale. • We extend the original deep framework to be suitable for multi-label datasets. • Extensive experimental results demonstrate the advantages of our method. Similarity-preserving hashing has become the mainstream of approximate nearest neighbor (ANN) search for large-scale image retrieval. Recent research shows that deep neural networks can produce efficient feature representation. Most existing deep hashing schemes simply utilize the middle-layer features of the deep neural networks to measure the similarity between query images and database images. However, these visual features are suboptimal for discriminating the semantic information of images, especially for complex images that contain multiple objects. In this paper, a deep framework is employed to learn multi-level non-linear transformations to obtain advanced image features, and then we combine these intermediate features and top layer visual information to implement image retrieval. Three criterions are enforced on these compact codes: (1) minimal quantization loss; (2) evenly distributed binary; (3) independent bits. The experimental results on five public large-scale datasets demonstrate the superiority of our method compared with several other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Abstract Perceptual quality evaluation of multiply distorted images has become a very challenging research topic. In this paper, we present a novel and efficient deep blind quality evaluator for multiply distorted images based on monogenic binary coding (MBC). Local complementary structural information and a deep learning method are employed to blindly evaluate the quality of multiply distorted images. First, a monogenic signal representation is utilized to decompose a multiply distorted image into three complementary components: orientation, phase, and magnitude. The quality-predictive features are then determined from the complementary components. Finally, the features are mapped to the human quality score of the multiply distorted image based on the deep neural network. The results on two newly established multiply distorted image subjective databases confirm that our metric has a better prediction performance than existing state-of-the-art full-reference and classical blind metrics. [ABSTRACT FROM AUTHOR]

Highlights • A structure representation based contrast-distorted quality metric is proposed. • The statistical structural features are extracted on the S-CIELAB color space. • Experiments have shown the superiority and applications of the proposed method. Abstract In many real-world applications, images are prone to be degraded by contrast distortions during image acquisition. Quality assessment for contrast-distorted images is vital for benchmarking and optimizing the contrast-enhancement algorithms. To this end, this paper proposes a no-reference quality metric for contrast-distorted images based on Multifaceted Statistical representation of Structure (MSS). The "Multifaceted" has two meanings, namely (1) not only the luminance information, but also the chromatic information is used for structure representation. This is inspired by the fact that the chromatic information on the one hand affects the perception of image quality as well, and on the other hand it changes along with the contrast distortions. Therefore, the chromatic information should be integrated with the luminance information for quality assessment of contrast-distorted images, a fact most existing quality metrics overlook; (2) regarding structure representation, three aspects, i.e. spatial intensity, spatial distribution, and orientation of structures are calculated, which is enlightened by the fact that the human visual system (HVS) is sensitive to the three aspects of structures. Specifically, the image is first transformed from RGB to the S-CIELAB color space to obtain a representation that is more consistent with the characteristics of the HVS, as well as to separate the chromatic information from the luminance. Then the statistical structural features are extracted from both luminance and chromatic channels. Finally, the back propagation (BP) neural network is adopted to train a quality prediction model. Experimental results conducted on four public contrast-distorted image databases demonstrate the superiority of the proposed method to the relevant state-of-the-arts. [ABSTRACT FROM AUTHOR]