Open-set gait recognition presents a critical challenge for real-world identity authentication systems, requiring simultaneous identification of known users and detection of unknown users under practical deployment conditions. However, in practical Wi-Fi sensing environments, signal noise, clothing variation, and multipath interference often blur the boundary between known and unknown classes, making traditional closed-set methods inadequate. To address this challenge, GUARD is proposed as a unified open-set and closed-set gait recognition framework based on feature reconstruction. The core idea is that known-class samples can be accurately reconstructed under matched label conditions, while unknown samples yield significantly higher reconstruction errors due to label mismatch, thereby providing a discriminative signal for open-set recognition. To enhance the stability and discriminability of features, GUARD integrates a Global Temporal Attention (GTA) mechanism to capture long-range temporal dependencies, and introduces a Pseudo-Gaussian Enhanced Self-Attention (PGESA) module that models dynamic attention distributions via Gaussian approximation, enabling selective emphasis on salient temporal features while effectively suppressing background noise. Additionally, a feature extractor locking strategy is employed to freeze identity-relevant representations once closed-set performance is optimized, preventing degradation during open-set training. Experimental results show that GUARD achieves over 20% improvement in open-set recognition rate, while maintaining approximately 95% closed-set accuracy, demonstrating superior robustness and generalization in complex sensing environments.

Accurately segmenting various clinically significant lesion areas from whole-body computed tomography (CT) scans is crucial for automated diagnosis and treatment planning. Training an automatic segmentation model effectively is desirable, but it heavily relies on a large scale of pixel-wise labeled data, which is laborious, time-consuming, and expensive to obtain. Existing weakly-supervised segmentation approaches often struggle with regions nearby the lesion boundaries. This paper proposes a target-level incomplete annotation (TIA) for medical image annotation and a multi-lesion segmentation framework. TIA annotates only one complete target region per slice to accurately capture boundaries with minimal annotated effort. Multi-lesion segmentation framework is a weakly supervised learning method, which first implements a medical cut-paste segmentation branch to provide images with pure target pixels and boundaries for training the lesion segmentation model, second utilizes prior anatomical information in the prior-assisted target localization branch to locate and identify target regions, third generates high-confidence pseudo-labels by combining the outputs of cut-paste segmentation branch and prior-assisted target localization branch. A graph neural network (GNN) is adopted to correct noisy labels and propagate reliably labeled pixels to unlabeled pixels. By utilizing TIA, our framework can achieve state-of-the-art results for medical image segmentation, which is validated on Crohn's dataset.

With integrated sensing and communications (ISAC) and reconfigurable intelligent surface (RIS) emerging as critical enablers for future mobile communications, their combination has attracted increasing attention lately. To effectively utilize RIS for improving ISAC, we propose two novel designs targeting different scenarios. The first design strikes for a spectral-efficient ISAC by seeking to maximize the weighted sum rate (WSR) of communications and minimize sensing radiation pattern approximation error. The second design aims to achieve an energy-efficient ISAC by optimizing the power allocation between communications and sensing subject to communication quality of service (QoS) constraints. Different optimization problems are formulated for the two designs, with practical constraints of RIS considered, including unit modulus and discrete phase shift. Efficient solutions are developed for the non-convex optimization problems by adeptly employing techniques including weighted minimum mean squared error (WMMSE), fractional programming (FP), second-order cone programming (SOCP), semi-definite relaxing (SDR), and feasibility check. Simulation results demonstrate the non-trivial improvements of WSR, communication energy efficiency and sensing radiation patterns achieved by the proposed designs, also highlighting their superiority over the conventional methods.

Wi-Fi signal-based person identification has become a hot research topic due to the widespread deployment of Wi-Fi devices and the fact that these approaches are noncontact, passive, and privacy-preserving. While the existing related methods and systems have achieved good performance for person identification, they also encounter many significant challenges in practical applications. Due to the propagation properties of Wi-Fi signals, the signal at the receiver will change significantly when the user’s appearance changes. This makes single-appearance trained models unusable for cross-appearance recognition tasks. To address this challenge, we propose a deep learning-based framework for appearance-independent identification using Wi-Fi signals (WiAi-ID), the core of which lies in the fact that the domain discriminator and feature extractor are trained together in an adversarial manner, thus forcing the model to extract identity-inherent features independent of human appearance, and introduces a multiscale CNN adaptation module to capture time-span-based features. We collected Wi-Fi signal data of pedestrians with different appearances. The experimental results show that WiAi-ID can effectively eliminate the impact on identification due to pedestrian appearance variations and accordingly outperforms the current state-of-the-art video and wireless signal-based recognition methods.

Wi-Fi CSI-based gait recognition is a non-intrusive passive biometric identification technology that has garnered significant attention in the fields of security and smart furniture due to its user-friendly nature. However, in practical application scenarios, gait recognition systems face the challenge of reliably identifying subjects across different scenes or states. To overcome this challenge, this paper proposes DCS-Gait, a domain adaptation solution for cross-scene and cross-state gait recognition based on Wi-Fi CSI. DCS-Gait leverages a novel data distribution measurement called Cross-Attention Metric to align the class-level data distribution differences, enabling the model to learn invariant features across scenes and states. To address the issue of data annotation, we employ a pre-training method to obtain pseudo labels for the dataset. Additionally, a combined matching filtering technique is utilized to generate high-quality pseudo labels for unrecognized data, which can be further employed for supervised model training. We evaluated the effectiveness of DCS-Gait on a large test set consisting of 34 subjects, 2 scenes, and 3 different states, and the results demonstrate significant improvements over the state-of-the-art baselines in both cross-scene and cross-state gait recognition tasks. DCS-Gait provides a promising and reliable solution for accurate cross-scene and cross-state gait recognition in real-world settings.

To accurately segment various clinical lesions from computed tomography(CT) images is a critical task for the diagnosis and treatment of many diseases. However, current segmentation frameworks are tailored to specific diseases, and limited frameworks can detect and segment different types of lesions. Besides, it is another challenging problem for current segmentation frameworks to segment visually inconspicuous and small-scale tumors (such as small intestinal stromal tumors and pancreatic tumors). Our proposed framework, CDI-NSTSEG, efficiently segments small non-salient tumors using multi-scale visual information and non-local target mining. CDI-NSTSEG follows the diagnostic process of clinicians, including preliminary screening, localization, refinement, and segmentation. Specifically, we first explore to extract the unique features at three different scales (1×, 0.5×, and 1.5×) based on the scale space theory. Our proposed scale fusion module (SFM) hierarchically fuses features to obtain a comprehensive representation, similar to preliminary screening in clinical diagnosis. The global localization module (GLM) is designed with a non-local attention mechanism. It captures the long-range semantic dependencies of channels and spatial locations from the fused features. GLM enables us to locate the tumor from a global perspective and output the initial prediction results. Finally, we design the layer focusing module (LFM) to gradually refine the initial results. LFM mainly conducts context exploration based on foreground and background features, focuses on suspicious areas layer-by-layer, and performs element-by-element addition and subtraction to eliminate errors. Our framework achieves state-of-the-art segmentation performance on small intestinal stromal tumor and pancreatic tumor datasets.

Scene graph is a structured representation of a scene that can clearly express the objects, attributes, and relationships between objects in the scene. As computer vision technology continues to develop, people are no longer satisfied with simply detecting and recognizing objects in images; instead, people look forward to a higher level of understanding and reasoning about visual scenes. For example, given an image, we want to not only detect and recognize objects in the image, but also understand the relationship between objects (visual relationship detection), and generate a text description (image captioning) based on the image content. Alternatively, we might want the machine to tell us what the little girl in the image is doing (Visual Question Answering (VQA)), or even remove the dog from the image and find similar images (image editing and retrieval), etc. These tasks require a higher level of understanding and reasoning for image vision tasks. The scene graph is just such a powerful tool for scene understanding. Therefore, scene graphs have attracted the attention of a large number of researchers, and related research is often cross-modal, complex, and rapidly developing. However, no relatively systematic survey of scene graphs exists at present. To this end, this survey conducts a comprehensive investigation of the current scene graph research. More specifically, we first summarize the general definition of the scene graph, then conducte a comprehensive and systematic discussion on the generation method of the scene graph (SGG) and the SGG with the aid of prior knowledge. We then investigate the main applications of scene graphs and summarize the most commonly used datasets. Finally, we provide some insights into the future development of scene graphs.

Object detection (OD) is a crucial computer vision task that has seen the development of many algorithms and models over the years. While the performance of current OD models has improved, they have also become more complex, making them impractical for industry applications due to their large parameter size. To tackle this problem, knowledge distillation (KD) technology was proposed in 2015 for image classification and subsequently extended to other visual tasks due to its ability to transfer knowledge learned by complex teacher models to lightweight student models. This paper presents a comprehensive survey of KD-based OD models developed in recent years, with the aim of providing researchers with an overview of recent progress in the field. We conduct an in-depth analysis of existing works, highlighting their advantages and limitations, and explore future research directions to inspire the design of models for related tasks. We summarize the basic principles of designing KD-based OD models, describe related KD-based OD tasks, including performance improvements for lightweight models, catastrophic forgetting in incremental OD, small object detection, and weakly/semi-supervised OD. We also analyze novel distillation techniques, i.e. different types of distillation loss, feature interaction between teacher and student models, etc. Additionally, we provide an overview of the extended applications of KD-based OD models on specific datasets, such as remote sensing images and 3D point cloud datasets. We compare and analyze the performance of different models on several common datasets and discuss promising directions for solving specific OD problems.

Accurate road centerline extraction is very important for many vital applications. In the road extraction, the acquisition of labeled data is time-consuming; thus, there is only a small amount of labeled samples in reality. To solve the problem of limited labeled samples, a semi-supervised road centerline extraction is proposed, which incorporates high-level feature selection, Markov random field (MRF), and ridge transversal method. The proposed road extraction approach consists of three steps: multiple features extraction, semi-supervised road area extraction, and road centerlines extraction. To get more abstract and discriminative high-level features, we apply multiple-feature adaptive sparse representation in mid-level features in different views generated by different prototype sets. To obtain an accurate road area result, we combine the feature learning framework with MRF. Then, we integrate Gabor filters and nonmaxima suppression with the ridge transversal method to extract centerlines. It is verified the proposed method achieves comparable performance with the state-of-the-art methods in terms of visual and quantitative aspects.

Text-based CAPTCHAs remains a popular scheme for distinguishing between a legitimate human user and an automated program. This article presents a novel genetic text captcha solver based on the generative adversarial network. As a departure from prior text captcha solvers that require a labor-intensive and time-consuming process to construct, our scheme needs significantly fewer real captchas but yields better performance in solving captchas. Our approach works by first learning a synthesizer to automatically generate synthetic captchas to construct a base solver. It then improves and fine-tunes the base solver using a small number of labeled real captchas. As a result, our attack requires only a small set of manually labeled captchas, which reduces the cost of launching an attack on a captcha scheme. We evaluate our scheme by applying it to 33 captcha schemes, of which 11 are currently used by 32 of the top-50 popular websites. Experimental results demonstrate that our scheme significantly outperforms four prior captcha solvers and can solve captcha schemes where others fail. As a countermeasure, we propose to add imperceptible perturbations onto a captcha image. We demonstrate that our countermeasure can greatly reduce the success rate of the attack.

Despite several attacks have been proposed, text-based CAPTCHAs are still being widely used as a security mechanism. One of the reasons for the pervasive use of text captchas is that many of the prior attacks are scheme-specific and require a labor-intensive and time-consuming process to construct. This means that a change in the captcha security features like a noisier background can simply invalid an earlier attack. This paper presents a generic, yet effective text captcha solver based on the generative adversarial network. Unlike prior machine-learning-based approaches that need a large volume of manually-labeled real captchas to learn an effective solver, our approach requires significantly fewer real captchas but yields much better performance. This is achieved by first learning a captcha synthesizer to automatically generate synthetic captchas to learn a base solver, and then fine-tuning the base solver on a small set of real captchas using transfer learning. We evaluate our approach by applying it to 33 captcha schemes, including 11 schemes that are currently being used by 32 of the top-50 popular websites including Microsoft, Wikipedia, eBay and Google. Our approach is the most capable attack on text captchas seen to date. It outperforms four state-of-the-art text-captcha solvers by not only delivering a significant higher accuracy on all testing schemes, but also successfully attacking schemes where others have zero chance. We show that our approach is highly efficient as it can solve a captcha within 0.05 second using a desktop GPU. We demonstrate that our attack is generally applicable because it can bypass the advanced security features employed by most modern text captcha schemes. We hope the results of our work can encourage the community to revisit the design and practical use of text captchas.

Multi-view clustering has played a vital role in real-world applications. It aims to cluster the data points into different groups by exploring complementary information of multi-view. A major challenge of this problem is how to learn the explicit cluster structure with multiple views when there is considerable noise. To solve this challenging problem, we propose a novel Robust Auto-weighted Multi-view Clustering (RAMC), which aims to learn an optimal graph with exactly k connected components, where k is the number of clusters. ℓ1-norm is employed for robustness of the proposed algorithm. We have validated this in the later experiment. The new graph learned by the proposed model approximates the original graphs of each individual view but maintains an explicit cluster structure. With this optimal graph, we can immediately achieve the clustering results without any further post-processing. We conduct extensive experiments to confirm the superiority and robustness of the proposed algorithm.

The lines in topographic maps are difficult to be separated from each other because of their confusing colors. To solve this problem, we propose a novel line separation method using their regional color and spatial information. Firstly, we divide the lines into lots of circular regions with a certain diameter, and consider these regions as the basic processing units. Then based on a new concept of regional color confusion, we classify all the divided circular regions into two kinds of regions by whether the color is pure or mixed. Further, for pure color regions, a fuzzy clustering algorithm with Gaussian kernel can be used to cluster them into different lines based on their color information. Meanwhile, we determine the memberships of the mixed color regions according to their spatial relations with the clustered pure color regions. The concept of regional color confusion is proposed to reduce the influences of the confusing colors to line separation, and the spatial relations are utilized to solve the problems of the membership determination of the mixed color regions. The experimental results demonstrate that our method can achieve higher accuracy compare with other two state-of-the-art methods, which provides a novel idea for line element segmentation from scanned topographic maps.

Chinese calligraphy is a popular, highly esteemed art form in the Chinese cultural sphere and worldwide. Ink brushes are the traditional writing tool for Chinese calligraphy and the subtle nuances of brush movements have a great impact on the aesthetics of the written characters. However, mastering the brush movement is a challenging task for many calligraphy learners as it requires many years’ practice and expert supervision. This paper presents a novel approach to help Chinese calligraphy learners to quantify the quality of brush movements without expert involvement. Our approach extracts the brush trajectories from a video stream; it then compares them with example templates of reputed calligraphers to produce a score for the writing quality. We achieve this by first developing a novel neural network to extract the spatial and temporal movement features from the video stream. We then employ methods developed in the computer vision and signal processing domains to track the brush movement trajectory and calculate the score. We conducted extensive experiments and user studies to evaluate our approach. Experimental results show that our approach is highly accurate in identifying brush movements, yielding an average accuracy of 90%, and the generated score is within 3% of errors when compared to the one given by human experts.

In recent years, unsupervised two-dimensional (2D) dimensionality reduction methods for unlabeled large-scale data have made progress. However, performance of these degrades when the learning of similarity matrix is at the beginning of the dimensionality reduction process. A similarity matrix is used to reveal the underlying geometry structure of data in unsupervised dimensionality reduction methods. Because of noise data, it is difficult to learn the optimal similarity matrix. In this letter, we propose a new dimensionality reduction model for 2D image matrices: unsupervised 2D dimensionality reduction with adaptive structure learning (DRASL). Instead of using a predetermined similarity matrix to characterize the underlying geometry structure of the original 2D image space, our proposed approach involves the learning of a similarity matrix in the procedure of dimensionality reduction. To realize a desirable neighbors assignment after dimensionality reduction, we add a constraint to our model such that there are exact [Formula: see text] connected components in the final subspace. To accomplish these goals, we propose a unified objective function to integrate dimensionality reduction, the learning of the similarity matrix, and the adaptive learning of neighbors assignment into it. An iterative optimization algorithm is proposed to solve the objective function. We compare the proposed method with several 2D unsupervised dimensionality methods. K-means is used to evaluate the clustering performance. We conduct extensive experiments on Coil20, AT&T, FERET, USPS, and Yale data sets to verify the effectiveness of our proposed method.

It is difficult to separate the point symbols from the scanned topographic maps accurately, which brings challenges for the recognition of the point symbols. In this paper, based on the framework of generalized Hough transform (GHT), we propose a new algorithm, which is named shear line segment GHT (SLS-GHT), to recognize the point symbols directly in the scanned topographic maps. SLS-GHT combines the line segment GHT (LS-GHT) and the shear transformation. On the one hand, LS-GHT is proposed to represent the features of the point symbols more completely. Its R-table has double level indices, the first one is the color information of the point symbols, and the other is the slope of the line segment connected a pair of the skeleton points. On the other hand, the shear transformation is introduced to increase the directional features of the point symbols; it can make up for the directional limitation of LS-GHT indirectly. In this way, the point symbols are detected in a series of the sheared maps by LS-GHT, and the final optimal coordinates of the setpoints are gotten from a series of the recognition results. SLS-GHT detects the point symbols directly in the scanned topographic maps, totally different from the traditional pattern of extraction before recognition. Moreover, several experiments demonstrate that the proposed method allows improved recognition in complex scenes than the existing methods.

Linear features are difficult to be separated from complicated background in color scanned topographic maps, especially when the color of linear features approximate to that of background in some particular images. This paper presents a method, which is based on energy density and the shear transform, for the separation of lines from background. First, the shear transform, which could add the directional characteristics of the lines, is introduced to overcome the disadvantage that linear information loss would happen if the separation method is used in an image, which is in only one direction. Then templates in the horizontal and vertical directions are built to separate lines from background on account of the fact that the energy concentration of the lines usually reaches a higher level than that of the background in the negtive image. Furthermore, the remaining grid background can be wiped off by grid templates matching. The isolated patches, which include only one pixel or less than ten pixels, are removed according to the connected region area measurement. Finally, using the union operation, the linear features obtained in different sheared images could supplement each other, thus the lines of the final result are more complete. The basic property of this method is introducing the energy density instead of color information commonly used in traditional methods. The experiment results indicate that the proposed method could distinguish the linear features from the background more effectively, and obtain good results for its ability in changing the directions of the lines with the shear transform.