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Due to the large intra-class differences between the same categories and the scale imbalance between different categories in the remote sensing image dataset, the semantic segmentation task presents the problem of small-scale object information loss, the imbalance between foreground and background, and simultaneously the background dominates, which seriously affects the performance of the network model. To solve the above problems, this paper proposes an efficient bilateral branch depth neural network model based on the U-Net depth neural network, named BBU-Net. Firstly, one branch of the network learns the distribution characteristics of the original data, and the other focuses on difficult samples. Then the two branches improve the representation and classification ability of the neural network by accumulating learning strategies. Finally, considering the geometric diversity of remote sensing images, this paper adopts test time augmentation and reflection padding strategies and proposes a balanced weighted loss function named CombineLoss to alleviate the imbalance in the training process. The depth neural network proposed in this paper was first tested on the Inria Aerial Image Labeling Dataset, and 87.53% of mean intersection over union and 97.4% of mean pixel accuracy were obtained, respectively. At the same time, to verify the model's complexity, the model proposed in this paper is compared with the neural network based on integrated learning. The comparison results show that the spatial complexity of the network proposed in this paper is much lower than the neural network obtained by integrated learning, and the parameters are also much smaller than the neural network based on integrated learning. Then use the satellite building dataset I in the WHU Building Dataset and mainstream semantic segmentation methods for multiple groups of comparative experiments. The experimental results show that the method proposed in this paper can effectively extract the semantic information of remote sensing images, significantly improve the imbalance of remote sensing image data, improve the performance of the network model, and achieve a good semantic segmentation effect, which fully proves the effectiveness of this method. [ABSTRACT FROM AUTHOR]

In the past decades, a variety of learning-based algorithms have been emerged to try to explore a better solution for stereo matching by leveraging various machine learning algorithms. For enriching learning-based stereo matching algorithm's methodologies, we cast the disparity estimation as a regression problem by leveraging Structured Support Vector Machine (SSVM) in this paper. There are three categories of features have been extracted on account of disparity cues for training the SSVM. Particularly, one of the three feature is named as 'Coarse-to-Fine PatchMatch Filtering', which effectively exploits region and pixel disparity cues. For attaining region disparity cues, we adopt MeshStereo and MeshStereo with Cross-Scale algorithms; for attaining pixel disparity cues, PatchMatch and Cross-Scale PatchMatch stereo matching algorithms are utilized. Performance evaluations on Middlebury v.2 and v.3 stereo data sets demonstrate that the proposed algorithm reveals comparable accuracy with other challenging learning-based ones. It is worth pointing out that our proposal performs over several orders of magnitude faster than others on training time. [ABSTRACT FROM AUTHOR]

Being a significant repository of Buddhist imagery, Thangka images are valuable historical materials of Tibetan studies, which covers many domains such as Tibetan history, politics, culture, social life and even traditional medicine and astronomy. Thangka cultural element images are the essence of Thangka images. Hence, Thangka cultural element images classification is one of the most important works of knowledge representation and mining in the field of Thangka and is the foundation of digital protection of Thangka images. However, due to the limited quantity, high complexity and the intricate textures of Thangka images, the classification of Thangka images is limited to a small number of categories and coarse granularity. Thus, a novel fusion texture feature dual-branch Thangka cultural elements classification model based on the attention mechanism and self-supervised contrastive learning has been proposed in this paper. Specifically, to address the issue of insufficient labeled samples and improve the classification performance, this method utilizes a large amount of unlabeled irrelevant data to pre-train the feature extractor through self-supervised learning. During the fine-tuning stage of the downstream task, a dual-branch feature extraction structure incorporating texture features has been designed, and MS Triplet Attention proposed by us is used for the integration of important features. Additionally, to address the problem of sample imbalance and the existence of a large number of difficult samples in the Thangka cultural element dataset, the Gradient Harmonizing Mechanism Loss has been adopted, and it has been improved by introducing a self-designed adaptive mechanism. The experimental results on Thangka cultural elements dataset prove the superiority of the proposed method over the state-of-the-art methods. The source code of our proposed algorithm and the related datasets is available at https://github.com/WiniTang/MS-BiCLR. [ABSTRACT FROM AUTHOR]

Fabric defect detection is an important part of the textile industry to ensure product quality. To solve the problems such as the difficulty of detecting small defects and the coexistence of multi-scale defects in fabric defect detection, a fabric defect detection method based on the feature purification fusion structure is proposed in this paper. Specifically, we improve the feature extraction network to enhance the network's ability to focus on small defective features and effectively reduce the model parameters. The existing methods use direct fusion between multi-level feature maps, which will lead to feature confusion. Therefore, we propose the feature purification fusion structure (FPF), which includes the semantic information supplementation strategy (SIS) and the detail information supplementation strategy (DIS). SIS extracts valid information from the deep feature map and supplements it to the shallow feature map, weakening feature information irrelevant to the shallow feature map detection task. DIS adaptively supplements the feature information required by the deep feature map detection task from the shallow feature map. FPF improves the ability of the network to detect small defects and effectively mitigates the aliasing effect generated during feature fusion. The experimental results show that compared to the baseline model YOLOv5s algorithm, our model achieved a 6.8% improvement in detection accuracy, with an average inference frame rate of 37.6 FPS, demonstrating better detection performance in fabric defect detection. Furthermore, extending this model to aluminum profile defect datasets also demonstrates strong performance. [ABSTRACT FROM AUTHOR]

The strip steel is a common metallic material with a wide range of applications in various industries. However, the issue of surface defects that possess high concealment and low discrimination, which arises during the process of inspecting the quality of strip steel, imposes limitations on the overall quality of strip steel products. The challenging task of industrial quality inspection stems from the difficulty and inefficiency of detecting these defects. This paper addresses the aforementioned challenges by introducing a fast segmentation network model called the feature reused network (FR-Net), which aims to improve the detection of small defects and enhance real-time detection performance in the strip steel surface quality inspection process. In FR-Net, a feature fusion module is used to construct a feature reused fusion bypass to improve the segmentation accuracy of small defects. In addition, a lightweight feature refinement module is proposed to enhance the expression capability of the feature extraction network without increasing the computational effort. Finally, an atrous spatial pyramid pooling module with residual connectivity is proposed to fuse deep features of different scales and enhance the perception of objects of different scales. Experiments on the publicly available datasets showed that the proposed FR-Net achieved the mean intersection over union (mIoU) for NEU-Seg and SD-Saliency-900 datasets were 84.53% and 87.24%, respectively. Meanwhile, the detection speed on a single GPU was 58 FPS. Finally, the size of the proposed FR-Net model is only 45 MB, achieving a good trade-off between accuracy, speed, and model size, thus, providing a new solution for network model deployment on industrial equipment. [ABSTRACT FROM AUTHOR]

Image fusion refers to the process of synergistic combination of useful sensory information from multiple images to synthesize a composite image with greater information content and increased practical value. It aims to maximize pertinent information specific to a sensor while minimizing uncertainty and redundancy in the fused output. In this paper, the authors have proposed a simple yet cohesive framework for 2D greyscale image fusion using morphological differential features. The features are extracted using morphological open–close filters applied at multiple scales using an isotropic structuring element which brings out categorical bright and dark features from the source images. At each scale, the bright (and dark) differential features are mutually compared using higher-valued AC coefficients obtained in the DCT domain within a block. The scale-specific fused features are recursively added to form an image containing high-frequency information from all conceivable scales. The fused image is achieved by superimposing the cumulative feature image onto a suitable base image. The base image is obtained by using a morphological weighted version of pseudomedian filter over the source images using the largest homothetic of the structuring element. The superiority of the framework is empirically verified in different domains of fusion, i.e. multi-focus, multi-sensor, multi-exposure, and multi-spectral image fusion. The proposed approach has surpassed the state-of-the-art unified fusion algorithms in terms of qualitative and quantitative evaluation with a perfect resource-time trade-off. Furthermore, the proposed method has been extended to greyscale–colour and colour–colour image pairs qualifying it for anatomical–functional image fusion. [ABSTRACT FROM AUTHOR]

Image generation has always been one of the important research directions in the field of computer vision. It has rich applications in virtual reality, image design, and video synthesis. Our experiments proved that the proposed multi-style image generative network can efficiently generate high-quality images with different artistic styles based on the semantic images. Compared with the current state-of-the-art methods, the result generation speed of our proposed method is the fastest. In this paper, we focus on implementing arbitrary style transfer based on semantic images with high resolution ( 512 × 1024 ). We propose a new multi-channel generative adversarial network which uses fewer parameters to generate multi-style images. The network framework consists of a content feature extraction network, a style feature extraction network, and a content-stylistic feature fusion network. Our qualitative experiments show that the proposed multi-style image generation network can efficiently generate semantic-based, high-quality images with multiple artistic styles and with greater clarity and richer details. We adopt a user preference study, and the results show that the results generated by our method are more popular. Our speed study shows that our proposed method has the fastest result generation speed compared to the current state-of-the-art methods. We publicly release the source code of our project, which can be accessed at https://github.com/JuanMaoHSQ/Multi-style-image-generation-based-on-semantic-image. [ABSTRACT FROM AUTHOR]

The challenge of image reconstruction from very-low-resolution images is made exceedingly difficult by multiple degradation factors in practical applications. Traditional methods do not consider the interactions between these degradation factors, so the results are often insufficient. To reconstruct low-resolution blurry images, both super-resolution and deblurring processes must be applied. In this paper, we propose a joint super-resolution and a deblurring model with integrated processing of the degradation factors to obtain better image quality. The joint model includes two branches, a super-resolution module and deblurring module, and both of them share the same feature extraction module. The super-resolution module consists of multiple layers of residual blocks. The deblurring module supports the robustness of the super-resolution module through feature feed-back in the learning process, by introducing an image blurring feature description into the feature representation. To create modules with high magnification, the base two-branch model is also used in two stages with scale recursion. A second-stage deblurring module receives the output of the first-stage super-resolution module and improves the deblurring capability when the image is further magnified. The modules enhance each other, significantly improve the quality of very-low-resolution text images, and maintain a low model complexity. A step-by-step training strategy is applied to reduce second-stage training difficulty. Experiments show that our approach significantly outperforms state-of-the-art methods in terms of image quality and optical character recognition accuracy, and with a lower computational cost. [ABSTRACT FROM AUTHOR]

Logistics parcel detection technology is critical for unmanned sorting. YOLOv5, the state-of-the-art (SOTA) object detection model, is a classic network widely used in engineering. However, on the premise of fast and accurate detection of logistics parcels, it confronts certain challenges of high computing load and parameter quantity. To address these issues, this paper proposes a two-scale lightweight deep learning model named SFYOLOv5. In this article, a lightweight feature extraction module called Pruned-Shuffle-Block (PSB) is proposed. Meanwhile, a double-layer pyramid structure for the medium and large target detection is created in accordance with the image size distribution of logistics parcels. With this structure, the Floating Point of Operations (FLOPs) and parameters in feature extraction were significantly reduced. In addition, a down sampling module named Focus for Downsampling (FFD) is designed and attention modules are introduced to extract high-level semantic information in logistics parcels. These modules not only compensate for the loss caused by down sampling but also improve the mean Average Precision (mAP). Finally, the comparison experiment is performed by using the self-built logistics parcel dataset. The results show that the mAP of the model reaches 99.1%, the number of model parameters decreased by 92.14%, and the FLOPs decreased by 89.57% compared with the existing SOTA model. This model can be used in logistics parcel intelligent sorting. [ABSTRACT FROM AUTHOR]

The detection of surface defects is crucial to industrial manufacturing. In recent years, numerous detection methods based on computer vision have been successfully applied in the industry. However, industrial defect detection is still full of challenges. In one aspect, most of the industrial defects are extremely small. In another aspect, even though the intra-class defects have numerous similar elements, their outward appearances differ significantly. In this paper, we propose a dual-structure attention-based multi-level feature fusion network (DaMFFN) to address these two issues. In the first attention-based multi-level feature extraction structure, we introduce novel attention pooling to preserve more detailed information about the defective features of the tiny defect by giving certain regions varying weights. In the second attention-based multi-level feature fusion structure, we propose channel attention to capture the defect feature with the greatest potential for discrimination rather than all possible defect features, which is employed to prevent the incorrect detection of intra-class defects. The experiments demonstrate that the detection performance of the DaMFFN is better than other methods in five surface defect datasets. [ABSTRACT FROM AUTHOR]

Shape recognition is an active research topic in the field of computer vision and graphic computing. Nevertheless, existing methods are still poor in accuracy and efficiency in some extent, which greatly limits their application in computer vision system. This paper investigates the restraint of feature structure that intrinsically deteriorates recognition performance. Furthermore, we propose a fast shape recognition method based on a bi-level restraint reduction of contour coding (CC2RR), which provides more effective theoretical support for the practical application of the visual algorithm. CC2RR reduces restraint performed from contour feature extraction and expression, respectively. First, for shape contour, the restraint of contour feature extraction is reduced by transforming the direction of contour points to contour segments; second, for the encoded contour segment, the restraint of the contour feature expression is reduced; in other words, the current direction is reduced to the previous and the next direction. Guided by these insights, Hamming code distance is used to match the coding features after the twofold restraint reduction, and the results are obtained. Experimental results verify that the method significantly improves the performance, which runs up to 500 times faster than the existing description methods based on shape contours while increasing robustness. This makes the method useful in practical software system. [ABSTRACT FROM AUTHOR]

In the real-world 24/7 surveillance systems, the images collected during the day and night are visible light images and infrared images, respectively. Infrared images lack color and texture information. In this case, it is more practical to use cross-modality person re-identification (re-ID) to process visible-infrared images. In fact, the cross-modality semantic alignment and specific discriminative feature extraction of different modalities are important for the improvement of modal performance. Therefore, a Semantic Consistent Feature Construction and Multi-granularity Feature learning (SCC–MGL) method is proposed for visible-infrared person re-ID in this paper. The SCC–MGL consists of a Semantic Consistent Feature Construction (SCC) module and a Multi-Granularity Information Enhancement (MGIE) module. In SCC, the features of different modalities are guided by analyzing the relation between feature maps channels and pedestrian's body parts to form consistent semantic information on the corresponding channels, which reduces the impact caused by the misalignment of semantic information. In MGIE, a local modality difference elimination strategy is proposed to remove the modality difference. Meanwhile, the local feature discrimination is improved by reasonably constraining multi-granularity features. The effectiveness and superiority of proposed method are validated by experimental results from SYSU-MM01 and RegDB datasets. [ABSTRACT FROM AUTHOR]

Multiple object tracking is challenging due to the complex spatiotemporal relationship and the occlusion of different targets. Most existing methods use separate neural networks to generate robust features for data association inside the targets' bounding boxes. Unlike existing methods that just consider each target and the trajectory formed independently while ignoring the context information between tracklet and intra-frame detection, this paper proposes a tracking algorithm that combines multi-channel features with learnable graph matching. In brief, we use global and local salient features to model the appearance of intra-frame targets based on parallel graphs and mining high-order intra-context relationships using a completely undirected graph relationship between trajectories and detections. On this basis, we establish a multi-channel message communication mechanism through the maximum pooling mechanism to transmit the maximum dissimilation registration results to the graph matching layer, thereby forming a more robust allocation result in the data association process. Lastly, the evaluations on MOTChallenge benchmarks verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Few-shot text style transfer faces two main challenges: The first challenge is the limited availability of reference style text, while the second challenge is the varying degrees of differences between the style reference image and the source image. Existing methods mainly focus on the influence of local style and global style feature extraction on text style, but they ignore the crucial role played by the difference between the style reference image and the source image on style characteristics, especially in Chinese, which has its own unique ideograph structure. To address this issue, this paper proposes Layer Similarity Guiding Few-shot Chinese Style Transfer (LSG-FCST). LSG-FCST can not only build a transfer network by encoding the content and style characteristics from low-level to high-level semantics, but it can also discover the similarity characteristics between the style reference image and the source image through the attention mechanism. Furthermore, LSG-FCST can integrate the style features generated by the similarity features of different layers and generate the target image through asymmetric decoding. In the self-built text image dataset, we consider three types of visibility situations for the test images in the training set: seen fonts unseen characters, unseen fonts seen characters, and unseen fonts unseen characters. The experiments show that LSG-FCST outperforms the state-of-the-art methods. The code and dataset can be accessed at https://github.com/LYM1111/LSG-FCST. [ABSTRACT FROM AUTHOR]

Deep learning shows great potential in low-light image enhancement, which can improve image brightness and contrast while keeping image natural. However, due to the lack of prior knowledge extracted manually or excessive amplification of noise, these methods result in poor quality of enhanced images. To solve these challenging problems, this paper proposes a dual-branch grayscale image enhancement network based on dense residual and attention mechanism. Low-light grayscale image is used as input. Firstly, features that fuse deep and shallow information are extracted through dense residual convolution branch network; secondly, texture features are extracted through U-Net branch network combined with attention mechanism; then the extracted features are integrated, and finally luminance is adjusted by a brightness adjustment module to output an enhanced grayscale image. In addition, a joint loss function is designed to measure the network training loss from brightness, texture, contrast and noise. A large number of quantitative and qualitative experiments on LOL and VE-LOL datasets show that the proposed method improves Peak Signal to Noise Ratio Index and Structural Similarity Index by 19.65 - 59.76% and 5.61 - 85.53%, respectively, compared with EnlightenGAN, KinD++, RUAS, LLFlow, etc. The proposed method is superior to the most famous methods, thanks to the deep feature extraction and fusion capability of dense residual convolutional network and texture extraction capability of U-Net network combined with attention mechanism. [ABSTRACT FROM AUTHOR]

Strong demand for autonomous vehicles and the wide availability of 3D sensors are continuously fueling the proposal of novel methods for 3D object detection. In this paper, we provide a comprehensive survey of recent developments from 2012–2021 in 3D object detection covering the full pipeline from input data, over data representation and feature extraction to the actual detection modules. We introduce fundamental concepts, focus on a broad range of different approaches that have emerged over the past decade, and propose a systematization that provides a practical framework for comparing these approaches with the goal of guiding future development, evaluation, and application activities. Specifically, our survey and systematization of 3D object detection models and methods can help researchers and practitioners to get a quick overview of the field by decomposing 3DOD solutions into more manageable pieces. [ABSTRACT FROM AUTHOR]

Object detection from UAV (unmanned aerial vehicle) images is a crucial and challenging task in the field of computer vision. The task suffers from the difficulties of small dense objects, low pixel occupation of objects, and features that are not easily extracted in images. In this paper, we proposed a multilayer feature fusion algorithm named SCA-YOLO (spatial and coordinate attention enhancement YOLO) for small object detection with hybrid attention mechanisms. It uses the single-stage detection algorithm YOLOv5 as the base framework. Firstly, a hybrid attention module with associated coordinate attention is designed to enhance the feature extraction of small objects. Secondly, to address the problem that small objects are vulnerable to being disturbed by the complex background information on UAV images, an improved SEB (simple and efficient bottleneck) module is designed to further distinguish foreground and background features. Thirdly, a multilayer feature fusion structure is built to perform channel stitching of shallow and deep feature maps, as well as to enrich the semantic information of shallow features by adding horizontal jump connections. Finally, experiments are conducted on the VisDrone2020 dataset, which involves a large number of small objects photographed by drones. In addition, we also conduct extended experiments on the DOTA dataset and PASCAL VOC dataset. Comparative experimental results indicate that the proposed method considerably improves the accuracy of small object detection on multiple benchmark datasets. [ABSTRACT FROM AUTHOR]

3D object detection, as an essential part of perception, plays a principal role in the autonomous driving system. The cost-competitive monocular 3D object detection has drawn increasing attention recently. However, it still suffers an inferior accuracy especially for occluded objects due to the limited camera view. Inspired by compositional models, in which an object is represented as a combination of multiple components, this paper proposes a new monocular 3D object detection method that decreases the impact of occlusion by utilizing an object's front and side cues. To do this, the features are extracted from a decoupled front and side representation and then fused by an attention-based module to obtain a more consistent feature distribution. An uncertainty-guided depth ensemble based on geometry is further applied to refine the depth prediction. Experiment results demonstrate that as compared to the conventional methods, the proposed method significantly improves the detection performance for occluded objects while still satisfying real-time efficiency, with the Average Precision on 40 recall positions (AP40), respectively, increasing by 10.23% for partly occluded objects and 12.22% for mostly occluded objects in the KITTI benchmark. The codes are released at https://github.com/kagurua/Front-Side-Det [ABSTRACT FROM AUTHOR]

In recent years, due to the powerful feature extraction capabilities of convolutional neural networks (CNNs), many single-image super-resolution (SISR) methods based on CNN have achieved remarkable results. However, more model parameters and higher computational cost make these methods unsuitable for devices with limited computing power. In this paper, a lightweight multi-dimension feature fusion network (LMDFFN) is proposed to reduce model parameters. In nonlinear mapping module of the LMDFFN, the lightweight feature extraction block (LFEB) and the multi-dimensional feature extraction block (MDFEB) embedded in each LFEB are designed to significantly improve the reconstruction performance. The channel split operation can group the features. Combined with square-kernel convolution kernel and asymmetric convolution kernel, different types of convolution kernels can focus on different types of features, and the grouping mechanism can effectively reduce the complexity of the model. In addition, a channel and spatial attention module (CSAM) dedicated to SISR is proposed to focus on image details and different semantic level in feature maps. Moreover, a lightweight reconstruction module is designed with depth-wise separable convolution, and the designed spatial attention module is integrated into the reconstruction module to focus on more important features in the reconstruction stage. Compared with recent lightweight SISR methods, LMDFFN has better reconstruction accuracy with lower model parameters and computational complexity, and the reconstructed images have higher visual quality. [ABSTRACT FROM AUTHOR]

This paper presents a novel and efficient multitask learning framework for image translation and saliency detection from remote sensing images, which mainly contains the image translation network-weight sharing attention GAN (WSA-GAN) and the salient object detection network-boundary guidance network (BGNet). WSA-GAN can be used to generate a large number of synthetic infrared remote sensing images (IRIs) or optical remote sensing images (ORIs) from the corresponding complementary modality images. Then, a new multimodal context-aware learning is proposed for feature extraction and to coordinate the entanglement of latent features in the multimodal context of ORIs and IRIs. Since convolutional neural networks do not perform well when the object has directional variance, our framework introduces the attention-aware CapsNet (AACNet) to alleviate the problem and enhance the feature expressiveness. In addition, knowledge distillation strategy is introduced in AACNet to reduce the model complexity. Finally, the multiscale feature learning network and the boundary-aware block are designed to generate more accurate saliency detection results with clear boundaries. Experimental results demonstrate that the presented image translation and salient object detection networks outperform other approaches. [ABSTRACT FROM AUTHOR]

Underwater object detection is a fascinating but challengeable subject in computer vision. Features are difficult to extract due to the color cast and blur of underwater images. Moreover, given the small scale of the underwater object, some details will be lost after several layers of convolution. Therefore, a multi-scale aggregation feature pyramid network is proposed to integrate multi-scale features and improve underwater object detection performance. Specifically, a lightweight and efficient network is used to extract the basic features. A special subnet is designed to improve the feature extraction capability of the backbone network to enrich the detailed features of small underwater objects. In addition, a multi-scale feature pyramid is proposed to enrich feature map. Each feature map enhances contextual information through a combination of up-sampling and down-sampling. The centerness strategy of the fully convolutional one-stage object detection head is improved by adding corner point regression to enhance the recall rate of small objects. Generalized intersection over union (GIoU) instead of IoU can better reflect the degree of coincidence between the actual box and the predicted box. Therefore, the regression loss is changed to GIoU loss. This paper evaluates the network on the underwater image dataset and obtains 78.90% mAP. Meanwhile, the experiment on the PASCAL VOC datasets is conducted and gets 84.3% mAP. [ABSTRACT FROM AUTHOR]

In order to address the problem of low efficiency and accuracy of artificial visual inspection for the quality of toy sets, this paper investigates the defect detection of toy sets based on machine vision. Firstly, an improved U-Net (IU-Net) is developed by introducing the Squeeze-and-Excitation (SE) block into the U-Net and modifying its objective function. Then, a convolutional neural network (CNN) is constructed for feature extraction and defect matching. Finally, a defect detection model for toy sets is created by integrating these two models. In addition, when it comes to a new-variety toy set, the IU-Net model is trained through transfer learning with a small number of new-variety data samples. This can reduce the dependence on new-variety data samples and improve the efficiency of model training process. The experimental results show that the defect detection accuracy of the proposed method is 100% and 99.43% for the whole toy set and single component, respectively, which meets the requirements of industrial automation quality inspection of toy sets. [ABSTRACT FROM AUTHOR]

The geometric distortion in panoramic images significantly mediates the performance of saliency detection method based on traditional CNN. The strategy of dynamically expanding convolution kernel can achieve good results, but it also produces a lot of computational overhead in the process of reading the adjacency list, which decreases the computational efficiency. The appearance of graph convolution provides a new way to solve such problems. Although using graph convolution can effectively extract the structural features of the graph, it reduces the accuracy of the model resulting from ignoring the spatial features of the image signal. To this end, this paper proposes a construction method of the multi-scale graph structure of the panoramic image and a panoramic image saliency detection model composed of an image saliency feature extraction network and multi-scale saliency feature fusion network combining the image structure information and spatial information in the panoramic image. First, we establish a graph structure consisting of root and leaf nodes obtained by super-pixel segmentation at different scales and spherical Fibonacci sampling, respectively. Then, a feature extraction network composed of two graph convolution layers and two one-dimensional auto-encoders with the same parameterization is used to extract the salient features of the multi-scale graph structure. Finally, the U-Net network fuses the multi-scale saliency features to get the final saliency map. The results show that the proposed model performs better than the state-of-the-art models in terms of calculation speed and accuracy. [ABSTRACT FROM AUTHOR]

Micro-expression is an expression that reveals one's true feelings and can be potentially applied in various domains such as healthcare, safety interrogation, and business negotiation. The micro-expression recognition is thus far being judged manually by psychologists and trained experts, which consumes a lot of human effort and time. Recently, the development of the deep learning network has proven promising performance in many computer vision-related tasks. Amongst, micro-expression recognition adopts the deep learning methodology to improve the feature learning capability and model generalization. This paper introduces a lightweight apex-based enhanced network that improves by extending one of the state-of-the-art, shallow triple stream three-dimensional CNN. Concretely, the network is first pre-trained with a macro-expression dataset to encounter the small data problem. The features were extracted from CASME II, SMIC, and SAMM datasets. Moreover, thorough recognition results comparison of the datasets are the optical flow-guided features. Besides, an eye masking technique is introduced to reduce noise interference such as eye blinking and glasses reflection issues. The results obtained have an accuracy of 79.19 % and an F1-score of 75.9 % . Comprehensive experimentation had been conducted on the composite dataset that consists of is provided by comparing it with recent methods. Detailed qualitative and quantitative results are reported and discussed. [ABSTRACT FROM AUTHOR]

The traditional local binary pattern (LBP) is susceptible to the influence of the centre pixel and noise and cannot accurately identify wire rope surface defects. To solve this problem, an image segmentation-based central multiscale local binary pattern (ISCM-LBP) and grey level cooccurrence matrix (GLCM) feature fusion method is proposed in this paper for defect recognition. Image segmentation and multiple scales are introduced into the local binary pattern algorithm to improve the image detail description and suppress noise sensitivity. Second, the centre pixel is connected with the neighbourhood pixel to enhance the robustness of the centre pixel. To further improve the image integrity description, PCA dimensionality reduction and GLCM feature fusion are performed on the features extracted by the ISCM-LBP algorithm, and the steel wire rope surface defects are identified by a support vector machine classifier. Experimental results show that the overall recognition rate reaches 97.5%, which is at least 5% higher than that of other algorithms and can effectively identify various defects on the surface of wire rope. [ABSTRACT FROM AUTHOR]

This paper proposes an unconstrained human gaze estimation approach for medium-distance scene based on monocular vision. A recurrent convolution neural network for gaze estimation is designed to construct the mapping relationship from the face image to the 3D gaze vector in the camera space, so as to complete the estimation of the human gaze. Firstly, based on the VICON system and a monocular camera, the face image sequence and gaze vector sequence of human gaze behavior are collected in different situations of medium-distance scenes to construct the time-synchronized gaze estimation dataset. Secondly, the recurrent convolutional gaze estimation neural network is designed, and the head pose feature and eye appearance feature are extracted from single frame image with monocular vision image sequences as input. Thirdly, the extracted gaze features are fused in the spatial dimension and the temporal dimension to estimate the direction of the human gaze. Finally, the cross-validation and experimental evaluation of the estimation errors of the constructed gaze estimation model are carried out. The results show that the Cross-Person experimental average error is 7.65°, and the Person-Specific experimental error is 3.88°. Compared with the Gaze360 method, the proposed approach reduces the average evaluation error of the gaze angle by 5.0% in the Cross-Person experiments and 16.6% with the Person-Specific experiments. These experiments verify the effectiveness of the proposed approach, and it has better generalization ability and robustness. [ABSTRACT FROM AUTHOR]

Text-to-image generation is a challenging task. Although diffusion models can generate high-quality images of complex scenes, they sometimes suffer from a lack of realism. Additionally, there is often a large diversity among images generated from different text with the same semantics. Furthermore, the generation of details is sometimes insufficient. Generative adversarial networks can generate realism images. These images are consistent with the text descriptions. And the networks can generate content-consistent images. In this paper, we argue that generating images that are more consistent with the text descriptions is more important than generating higher-quality images. Therefore, this paper proposes the pretrained model-based generative adversarial network (PMGAN). PMGAN utilizes multiple pre-trained models in both generator and discriminator. Specifically, in the generator, the deep attentional multimodal similarity model text encoder extracts word and sentence embeddings from the input text, and the contrastive language-image pre-training (CLIP) text encoder extracts initial image features from the input text. In the discriminator, a pre-trained CLIP image encoder extracts image features from the input image. The CLIP encoder can map text and images into a common semantic space, which is beneficial to generate high-quality images. Experimental results show that compared to the state-of-the-art methods, PMGAN achieves better scores on both inception score and Fréchet inception distance and can produce higher quality images while maintaining greater consistency with text descriptions. [ABSTRACT FROM AUTHOR]