Pre-trained Vision-Language Models (VLMs) struggle with Zero-Shot Anomaly Detection (ZSAD) due to a critical adaptation gap: they lack the local inductive biases required for dense prediction and employ inflexible feature fusion paradigms. We address these limitations through an Architectural Co-Design framework that jointly refines feature representation and cross-modal fusion. Our method proposes a parameter-efficient Convolutional Low-Rank Adaptation (Conv-LoRA) adapter to inject local inductive biases for fine-grained representation, and introduces a Dynamic Fusion Gateway (DFG) that leverages visual context to adaptively modulate text prompts, enabling a powerful bidirectional fusion. Extensive experiments on diverse industrial and medical benchmarks demonstrate superior accuracy and robustness, validating that this synergistic co-design is critical for robustly adapting foundation models to dense perception tasks. The source code is available at https://github.com/cockmake/ACD-CLIP.
CL-CLIP: CLIP-Based Continual Learning Framework with Cost-Volume Category Decoupling for Object Detection
National College for Excellent Engineers, Beihang University（卓越工程师学院，北京航空航天大学）；AI Research, Qihoo 360（360人工智能研究院，奇虎360）；School of Electronic Information Engineering, Beihang University（电子信息学院，北京航空航天大学）；School of Cyber Science and Technology, Beihang University（网络安全科学与技术学院，北京航空航天大学）；School of Computer Science and Engineering, Beihang University（计算机科学与工程学院，北京航空航天大学）；State Key Laboratory of Media Convergence and Communication, Communication University of China（媒体融合与传播国家重点实验室，中国传媒大学）；Institute of Information Technology, Mathematics and Mechanics, Lobachebsky University（信息技术、数学与力学学院，洛瓦茨基大学）；School of Artificial Intelligence, Beihang University（人工智能学院，北京航空航天大学）
NS-Net: Decoupling CLIP Semantic Information through NULL-Space for Generalizable AI-Generated Image Detection
Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology（数字取证工程研究中心，教育部，南京信息科技大学）；University of Macau（澳门大学）
Proxy Robustness in Vision Language Models is Effortlessly Transferable
Chongqing Key Laboratory of Bio-perception（重庆生物感知实验室）；Multimodal Intelligent Information Processing, Chongqing University, Chongqing 401331, China（多模态智能信息处理，重庆大学，重庆401331，中国）；School of Microelectronics（微电子学院）；Communication Engineering, Chongqing University, Chongqing 401331, China（通信工程，重庆大学，重庆401331，中国）；School of Data Science, Lingnan University, Hong Kong（数据科学学院，岭南大学，香港）
A key challenge in self-supervised video representation learning is how to effectively capture motion information besides context bias. While most existing works implicitly achieve this with video-specific pretext tasks (e.g., predicting clip orders, time arrows, and paces), we develop a method that explicitly decouples motion supervision from context bias through a carefully designed pretext task. Specifically, we take the keyframes and motion vectors in compressed videos (e.g., in H.264 format) as the supervision sources for context and motion, respectively, which can be efficiently extracte…
One significant factor we expect the video representation learning to capture, especially in contrast with the image representation learning, is the object motion. However, we found that in the current mainstream video datasets, some action categories are highly related with the scene where the action happens, making the model tend to degrade to a solution where only the scene information is encoded. For example, a trained model may predict a video as playing football simply because it sees the field, neglecting that the subject is dancing as a cheerleader on the field. This is against our ori…
OptiSAR-Net++: A Large-Scale Benchmark and Transformer-Free Framework for Cross-Domain Remote Sensing Visual Grounding
South China Normal University（华南师范大学）；Hong Kong University of Science and Technology（香港科技大学）；Tongji University（同济大学）；Shanghai Research Institute for Intelligent Autonomous Systems（上海自主智能无人系统科学中心）；State Key Laboratory of Intelligent Autonomous Systems（智能自主系统国家重点实验室）；Frontiers Science Center for Intelligent Autonomous Systems（智能自主系统前沿科学中心）
Wuhan University（武汉大学）；MPI for Informatics（马克斯·普朗克信息学研究所）
Zero-shot semantic segmentation (ZS3) aims to segment the novel categories that have not been seen in the training. Existing works formulate ZS3 as a pixel-level zeroshot classification problem, and transfer semantic knowledge from seen classes to unseen ones with the help of language models pre-trained only with texts. While simple, the pixel-level ZS3 formulation shows the limited capability to integrate vision-language models that are often pre-trained with image-text pairs and currently demonstrate great potential for vision tasks. Inspired by the observation that humans often perform segm…
DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception
School of Computer Science and Technology, HIT, Shenzhen（哈尔滨工业大学深圳校区计算机科学与技术学院）；International Research Institute for Artificial Intelligence, HIT, Shenzhen（哈尔滨工业大学深圳国际人工智能研究院）；University of Chinese Academy of Sciences（中国科学院大学）
Shanghai Jiao Tong University（上海交通大学）；The Chinese University of Hong Kong（香港中文大学）
Contrastive learning has shown promising potential in self-supervised spatio-temporal representation learning. Most works naively sample different clips to construct positive and negative pairs. However, we observe that this formulation inclines the model towards the background scene bias. The underlying reasons are twofold. First, the scene difference is usually more noticeable and easier to discriminate than the motion difference. Second, the clips sampled from the same video often share similar backgrounds but have distinct motions. Simply regarding them as positive pairs will draw the mode…
When Detectors Forget Forensics: Blocking Semantic Shortcuts for Generalizable AI-Generated Image Detection
State Key Laboratory of Blockchain and Data Security, Zhejiang University（区块链与数据安全国家重点实验室，浙江大学）；National University of Singapore（新加坡国立大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification
School of Big Data & Software Engineering, Chongqing University（重庆大学大数据与软件学院）；College of Computer Science, Chongqing University（重庆大学计算机学院）；Fowler College of Business, San Diego State University（圣地亚哥州立大学福勒商学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）
