1 citations · 3 across the 8 of their papers we have counts for
7 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,…
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…
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,…
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…
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…
Hierarchical Prompts for Rehearsal-free Continual Learning
Yukun Zuo, Hantao Yao, Lu Yu +2
Continual learning endeavors to equip the model with the capability to integrate current task knowledge while mitigating the forgetting of past task knowledge. Inspired by prompt t…
Fine-Grained Knowledge Selection and Restoration for Non-Exemplar Class Incremental Learning
Jiang-Tian Zhai, Xialei Liu, Lu Yu +1
Non-exemplar class incremental learning aims to learn both the new and old tasks without accessing any training data from the past. This strict restriction enlarges the difficulty…