8 citations · 13 across the 10 of their papers we have counts for
4 papers · 1 filter
Class Balance Matters to Active Class-Incremental Learning
Zitong Huang, Ze Chen, Yuanze Li +6
Few-Shot Class-Incremental Learning has shown remarkable efficacy in efficient learning new concepts with limited annotations. Nevertheless, the heuristic few-shot annotations may…
IMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced Learning Tasks
Zitong Huang, Ze Chen, Bowen Dong +3
Model Weight Averaging (MWA) is a technique that seeks to enhance model's performance by averaging the weights of multiple trained models. This paper first empirically finds that 1…
Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning
Zitong Huang, Ze Chen, Zhixing Chen +6
Few-shot Class-Incremental Learning (FSCIL) aims to continuously learn new classes based on very limited training data without forgetting the old ones encountered. Existing studies…
ProtoCLIP: Prototypical Contrastive Language Image Pretraining
Delong Chen, Zhao Wu, Fan Liu +4
Contrastive Language Image Pretraining (CLIP) has received widespread attention, since its learned representations can be transferred well to various downstream tasks. During the t…