This paper studies the challenging person re-identification (Re-ID) task under the cloth-changing scenario, where the same identity (ID) suffers from uncertain cloth changes. To learn cloth- and ID-invariant features, it is crucial to collect abundant training data with varying clothes, which is difficult in practice. To alleviate the reliance on rich data collection, we reinforce the feature learning process by designing powerful complementary data augmentation strategies, including positive and negative data augmentation. Specifically, the positive augmentation fulfills the ID space by randomly patching the person images with different clothes, simulating rich appearance to enhance the robustness against clothes variations. For negative augmentation, its basic idea is to randomly generate out-of-distribution synthetic samples by combining various appearance and posture factors from real samples. The designed strategies seamlessly reinforce the feature learning without additional information introduction. Extensive experiments conducted on both cloth-changing and -unchanging tasks demonstrate the superiority of our proposed method, consistently improving the accuracy over various baselines.

Multi-view action recognition aims to identify action categories from given clues. Existing studies ignore the negative influences of fuzzy views between view and action in disentangling, commonly arising the mistaken recognition results. To this end, we regard the observed image as the composition of the view and action components, and give full play to the advantages of multiple views via the adaptive cooperative representation among these two components, forming a Dual-Recommendation Disentanglement Network (DRDN) for multi-view action recognition. Specifically, 1) For the action, we leverage a multi-level Specific Information Recommendation (SIR) to enhance the interaction among intricate activities and views. SIR offers a more comprehensive representation of activities, measuring the trade-off between global and local information. 2) For the view, we utilize a Pyramid Dynamic Recommendation (PDR) to learn a complete and detailed global representation by transferring features from different views. It is explicitly restricted to resist the fuzzy noise influence, focusing on positive knowledge from other views. Our DRDN aims for complete action and view representation, where PDR directly guides action to disentangle with view features and SIR considers mutual exclusivity of view and action clues. Extensive experiments have indicated that the multi-view action recognition method DRDN we proposed achieves state-of-the-art performance over powerful competitors on several standard benchmarks. The code will be available at https://github.com/51cloud/DRDN.

Single image deraining aims to remove rain perturbation while restoring the clean background scene from a rain image. However, existing methods tend to produce blurry and over-smooth outputs, lacking some textural details. Wavelet transform can depict the contextual and textural information of an image at different levels, showing impressive capability of learning structural information in the images to avoid artifacts, and thus has been recently explored to consider the inherent overlap of background and rain perturbation in both the pixel domain and the frequency embedding space. However, the existing wavelet-based methods ignore the heterogeneous degradation for different coefficients due to the inherent directional characteristics of rain streaks, leading to inter-frequency conflicts and compromised deraining results. To address this issue, we propose a novel Direction-aware Attention Wavelet Network (DAWN) for rain streaks removal. DAWN has several key distinctions from existing wavelet transform-based methods: 1) introducing the vector decomposition to parameterize the learning procedure, where the rain streaks are derived into the vertical (V) and horizontal (H) components to learn the specific representation; 2) a novel direction-aware attention module (DAM) to fit the projection and transformation parameters to characterize the direction-specific rain components, which helps accurate texture restoration; 3) exploring practical composite constraints on the structure, details, and chrominance aspects for high-quality background restoration. Our proposed DAWN delivers significant performance gains on nine datasets across image deraining and object detection tasks, exceeding the state-of-the-art method MPRNet by 0.88 dB in PSNR on the Test1200 dataset with only 35.5% computation cost.

Spiking neural networks (SNNs) have garnered significant attention for their potential in ultra-low-power event-driven neuromorphic hardware implementations. One effective strategy for obtaining SNNs involves the conversion of artificial neural networks (ANNs) to SNNs. However, existing research on ANN-SNN conversion has predominantly focused on image classification task, leaving the exploration of action recognition task limited. In this paper, we investigate the performance degradation of SNNs on action recognition task. Through in-depth analysis, we propose a framework called Scalable Dual Threshold Mapping (SDM) that effectively overcomes three types of conversion errors. By effectively mitigating these conversion errors, we are able to reduce the time required for the spike firing rate of SNNs to align with the activation values of ANNs. Consequently, our method enables the generation of accurate and ultra-low-latency SNNs. We conduct extensive evaluations on multiple action recognition datasets, including UCF-101 and HMDB-51. Through rigorous experiments and analysis, we demonstrate the effectiveness of our approach. Notably, SDM achieves a remarkable Top-1 accuracy of 92.94% on UCF-101 while requiring ultra-low latency (4 time-steps), highlighting its high performance with reduced computational requirements. The code for our SDM framework will be available at https://github.com/githuberyh/SDM .

Person re-identification (ReID) plays a crucial role in video surveillance with the aim to search a specific person across disjoint cameras, and it has progressed notably in recent years. However, visible cameras may not be able to record enough information about the pedestrian’s appearance under the condition of low illumination. On the contrary, thermal infrared images can significantly mitigate this issue. To this end, combining visible images with infrared images is a natural trend, and are considerably heterogeneous modalities. Some attempts have recently been contributed to visible-infrared person re-identification (VI-ReID). This paper provides a complete overview of current VI-ReID approaches that employ deep learning algorithms. To align with the practical application scenarios, we first propose a new testing setting and systematically evaluate state-of-the-art methods based on our new setting. Then, we compare ReID with VI-ReID in three aspects, including data composition, challenges, and performance. According to the summary of previous work, we classify the existing methods into two categories. Additionally, we elaborate on frequently used datasets and metrics for performance evaluation. We give insights on the historical development and conclude the limitations of off-the-shelf methods. We finally discuss the future directions of VI-ReID that the community should further address.