1 citations · 1 across the 3 of their papers we have counts for
8 papers
Locality Preserving Markovian Transition for Instance Retrieval
Jifei Luo, Wenzheng Wu, Hantao Yao +2
Diffusion-based re-ranking methods are effective in modeling the data manifolds through similarity propagation in affinity graphs. However, positive signals tend to diminish over s…
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…
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…
Cluster-Aware Similarity Diffusion for Instance Retrieval
Jifei Luo, Hantao Yao, Changsheng Xu
Diffusion-based re-ranking is a common method used for retrieving instances by performing similarity propagation in a nearest neighbor graph. However, existing techniques that cons…
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…