4 papers
SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect
Huaiyuan Zhang, Hang Chen, Yu Cheng +5
In this technical report, we present our solution to the CVPR 2025 Visual Anomaly and Novelty Detection (VAND) 3.0 Workshop Challenge Track 1: Adapt & Detect: Robust Anomaly Detect…
CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation
Lin Sun, Jiale Cao, Jin Xie +2
Contrastive Language-Image Pre-training (CLIP) exhibits strong zero-shot classification ability on various image-level tasks, leading to the research to adapt CLIP for pixel-level…
iSeg: An Iterative Refinement-based Framework for Training-free Segmentation
Lin Sun, Jiale Cao, Jin Xie +2
Stable diffusion has demonstrated strong image synthesis ability to given text descriptions, suggesting it to contain strong semantic clue for grouping objects. The researchers hav…
Robustar: Interactive Toolbox Supporting Precise Data Annotation for Robust Vision Learning
Chonghan Chen, Haohan Wang, Leyang Hu +6
We introduce the initial release of our software Robustar, which aims to improve the robustness of vision classification machine learning models through a data-driven perspective.…