Many studies have proposed attack methods to generate adversarial patterns for evading pedestrian detection, alarming the computer vision community about the need for more attention to the robustness of detectors. However, adversarial patterns optimized by these methods commonly have limited performance at medium to long distances in the physical world. To overcome this limitation, we identify two main challenges. First, in existing methods, there is commonly an appearance gap between simulated distant adversarial patterns and their physical world counterparts, leading to incorrect optimization. Second, there exists a conflict between adversarial losses at different distances, which causes difficulties in optimization. To overcome these challenges, we introduce a Full Distance Attack (FDA) method. Our physical world experiments demonstrate the effectiveness of our FDA patterns across various detection models like YOLOv5, Deformable-DETR, and Mask RCNN. Codes available at https://github.com/zhicheng2T0/Full-Distance-Attack.git

One major limitation of CNNs is that they are vulnerable to adversarial attacks. Currently, adversarial robustness in neural networks is commonly optimized with respect to a small pre-selected adversarial noise strength, causing them to have potentially limited performance when under attack by larger adversarial noises in real-world scenarios. In this research, we aim to find Neural Architectures that have improved robustness on a wide range of adversarial noise strengths through Neural Architecture Search. In detail, we propose a lightweight Adversarial Noise Estimator to reduce the high cost of generating adversarial noise with respect to different strengths. Besides, we construct an Efficient Wide Spectrum Searcher to reduce the cost of adjusting network architecture with the large adversarial validation set during the search. With the two components proposed, the number of adversarial noise strengths searched can be increased significantly while having a limited increase in search time. Extensive experiments on benchmark datasets such as CIFAR and ImageNet demonstrate that with a significantly richer search signal in robustness, our method can find architectures with improved overall robustness while having a limited impact on natural accuracy and around 40% reduction in search time compared with the naive approach of searching. Codes available at: https://github.com/zhicheng2T0/Wsr-NAS.git

Currently, Vision Transformer (ViT) and its variants have demonstrated promising performance on various computer vision tasks. Nevertheless, task-irrelevant information such as background nuisance and noise in patch tokens would damage the performance of ViT-based models. In this paper, we develop Sufficient Vision Transformer (Suf-ViT) as a new solution to address this issue. In our research, we propose the Sufficiency-Blocks (S-Blocks) to be applied across the depth of Suf-ViT to disentangle and discard task-irrelevant information accurately. Besides, to boost the training of Suf-ViT, we formulate a Sufficient-Reduction Loss (SRLoss) leveraging the concept of Mutual Information (MI) that enables Suf-ViT to extract more reliable sufficient representations by removing task-irrelevant information. Extensive experiments on benchmark datasets such as ImageNet, ImageNet-C, and CIFAR-10 indicate that our method can achieve state-of-the-art or competing performance over other baseline methods. Codes are available at: https://github.com/zhicheng2T0/Sufficient-Vision-Transformer.git

High-Resoultion Net(HRNet) maintains high-resolution feature maps during the deep feature extraction process, and performs well in many areas of computer vision. In this paper, HRNet is applied to the road extraction and building extraction tasks of remote sensing images, and the two loss functions of DICE loss and BCE (binary crossentroy) loss can be used to solve the problem of samples imbalance. When there are only two classes of segmentation objects, HRNet performs better than several methods used in remote sensing image segmentation area. Experiments on road and building datasets shows that HRNet has strong practicality in remote sensing image segmentation.