283 citations · 546 across the 35 of their papers we have counts for
5 papers · 2 filters
AREA: Attribute Extraction and Aggregation for CLIP-Based Class-Incremental Learning
Zhen-Hao Xie, Yu-Cheng Shi, Da-Wei Zhou
Class-Incremental Learning (CIL) is important in building real-world learning systems. In CLIP-based CIL, the model performs classification by comparing similarity between visual a…
Stable Routing for Mixture-of-Experts in Class-Incremental Learning
Zirui Guo, Quan Cheng, Da-Wei Zhou +1
Class-incremental learning (CIL) requires models to learn new classes sequentially while preserving prior knowledge. Recently, approaches that combine pre-trained models with mixtu…
Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning
Hao Sun, Zi-Jun Ding, Da-Wei Zhou
Class-Incremental Learning (CIL) enables models to continuously integrate new knowledge while mitigating catastrophic forgetting. Driven by the remarkable generalization of CLIP, l…
Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning
Tao Hu, Da-Wei Zhou
Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world deployment often requires continual capability expansion across seque…
Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning
Zhen-Hao Xie, Yan Wang, Hao Sun +3
Class-Incremental Learning (CIL) requires a learning system to learn new classes while retaining previously learned knowledge. However, in real-world scenarios such as autonomous d…