3 papers
cs.CV2025
Beyond CLIP Generalization: Against Forward&Backward Forgetting Adapter for Continual Learning of Vision-Language Models
Songlin Dong, Chenhao Ding, Jiangyang Li +4
This study aims to address the problem of multi-domain task incremental learning~(MTIL), which requires that vision-language models~(VLMs) continuously acquire new knowledge while…
cs.CV2024
Beyond Prompt Learning: Continual Adapter for Efficient Rehearsal-Free Continual Learning
Xinyuan Gao, Songlin Dong, Yuhang He +2
The problem of Rehearsal-Free Continual Learning (RFCL) aims to continually learn new knowledge while preventing forgetting of the old knowledge, without storing any old samples an…
cs.CV2024
CEAT: Continual Expansion and Absorption Transformer for Non-Exemplar Class-Incremental Learning
Xinyuan Gao, Songlin Dong, Yuhang He +2
In real-world applications, dynamic scenarios require the models to possess the capability to learn new tasks continuously without forgetting the old knowledge. Experience-Replay m…