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Author

Ke Xu

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Open access Aug 2026

Multi-level visual-language models feature learning for generalizable anomaly detection

A Multi-Level Feature Learning (MLFL) framework is proposed to enhance the zero-shot capability of CLIP via hierarchical alignment and introduces a Generalized Prompt Distillation (GPD) module that distills object-aware prompts into a unified representation.

Jianfeng Qiu, Junfa Li, Juan Xie et al. · 0 citations
Jul 2026

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment

Test-time adaptation (TTA) has emerged as a key strategy to enhance vision-language models (VLMs) under real-world distribution changes. However, existing methods always face two problems: 1) The fundamental trade-off dilemma: parameter-free TTA retains inference efficiency but fails to correct modality misalignment, w...

Shanshan Wang, .. ALuSi, Xun Yang et al. · 0 citations

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