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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]