5 papers · 1 filter
Complementary Text-Guided Attention for Zero-Shot Adversarial Robustness
Lu Yu, Haiyang Zhang, Changsheng Xu
Due to the impressive zero-shot capabilities, pre-trained vision-language models (e.g., CLIP), have attracted widespread attention and adoption across various domains. Nonetheless,…
Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning
Lu Yu, Zhe Tao, Dipam Goswami +4
Deep neural networks (DNNs) excel on fixed datasets but struggle with incremental and shifting data in real-world scenarios. Continual learning addresses this challenge by allowing…
Language Guided Concept Bottleneck Models for Interpretable Continual Learning
Lu Yu, Haoyu Han, Zhe Tao +2
Continual learning (CL) aims to enable learning systems to acquire new knowledge constantly without forgetting previously learned information. CL faces the challenge of mitigating…
SEP: Self-Enhanced Prompt Tuning for Visual-Language Model
Hantao Yao, Rui Zhang, Lu Yu +2
Prompt tuning based on Context Optimization (CoOp) effectively adapts visual-language models (VLMs) to downstream tasks by inferring additional learnable prompt tokens. However, th…
Text-Guided Attention is All You Need for Zero-Shot Robustness in Vision-Language Models
Lu Yu, Haiyang Zhang, Changsheng Xu
Due to the impressive zero-shot capabilities, pre-trained vision-language models (e.g. CLIP), have attracted widespread attention and adoption across various domains. Nonetheless,…