1 citations · 2 across the 5 of their papers we have counts for
5 papers
DRDN: Decoupled Representation Dynamic Network for From-Scratch ViT Class-Incremental Learning
Bingchen Huang, Yifu Chen, Zhiling Wang +1
Dynamic expansion methods for class-incremental learning (CIL) protect task-specific knowledge by growing dedicated tokens or subnetworks, yet our analyses suggest that classificat…
Retrieved Images as Visual Thought: Training-Free Multimodal In-Context Learning for the Open-vs-Closed Gap
Bingchen Huang, Zhiling Wang, Yifu Chen +1
Recent work on Thinking with Images makes vision a dynamic part of reasoning, but does so through generation: the model invokes external tools, synthesizes code, or imagines new im…
TDR: Task-Decoupled Retrieval with Fine-Grained LLM Feedback for In-Context Learning
Yifu Chen, Bingchen Huang, Zhiling Wang +4
In-context learning (ICL) has become a classic approach for enabling LLMs to handle various tasks based on a few input-output examples. The effectiveness of ICL heavily relies on t…
MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition
Tianlun Zheng, Zhineng Chen, BingChen Huang +2
Multilingual text recognition (MLTR) systems typically focus on a fixed set of languages, which makes it difficult to handle newly added languages or adapt to ever-changing data di…
Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning
Bingchen Huang, Zhineng Chen, Peng Zhou +2
The dynamic expansion architecture is becoming popular in class incremental learning, mainly due to its advantages in alleviating catastrophic forgetting. However, task confusion i…