3 papers
cs.CV2025
Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts
Yanning Hou, Ke Xu, Junfa Li +2
Recently, the powerful generalization ability exhibited by foundation models has brought forth new solutions for zero-shot anomaly segmentation tasks. However, guiding these founda…
cs.LG2025
DOTA: Distributional Test-Time Adaptation of Vision-Language Models
Zongbo Han, Jialong Yang, Guangyu Wang +4
Vision-language foundation models (VLMs), such as CLIP, exhibit remarkable performance across a wide range of tasks. However, deploying these models can be unreliable when signific…
cs.CV2025
StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detection
Yanning Hou, Yanran Ruan, Junfa Li +3
Enhancing the alignment between text and image features in the CLIP model is a critical challenge in zero-shot industrial anomaly detection tasks. Recent studies predominantly util…