Sep 2026· International Journal of Computer Vision· Vol 134· 0 citations· 54 references
Domain Adaptation and Few-Shot Learning
TL;DR
This work revisits catastrophic forgetting from an interpretability perspective and introduces correctness-guided layer alignment (CLA), which allows the new model to inherit multi-layer semantic consistency from its predecessor while learning from ground-truth supervision.
This work introduces Merging for Continual Unlearning (MCU), an approach that dynamically merges multiple one-shot unlearning adapters into a unified adapter upon receiving each new unlearning request and achieves superior unlearning effectiveness while preserving both retained knowledge and general multimodal utility.
Yuhang Wang, Lin-Lin Zhang, Haoxuan Ji et al.· 0 citations
ConvMem is a training-free, highly parallelizable framework that reformulates long-context reasoning as a hierarchical convolution, Inspired by CNNs, that outperforms training-free baselines and avoids the risk of overfitting to parametric priors often observed in RL-trained models on out-of-distribution tasks.
Hongming Zhang, Zhao-Zhen Gu, Fengshuo Bai et al.· 0 citations
DLMR is a parameter-efficient mechanism that equips MLLMs with Dual Latent Memories: a visual memory that compresses image evidence and a reasoning memory that tracks intermediate conclusions and constraints with only a small number of additional trainable parameters.
Hao-Xuan Ma, Jin-Fei Qi, Yi-Cheng Xiao et al.· 0 citations
This work introduces Continual Prompt Replay (CPR), which harnesses shared reasoning to improve learning on the arriving and future tasks by replaying previous-task prompts and regenerating their responses with the current policy.
Li-Rui Luo, Guo-Xi Zhang, Hong-Ming Xu et al.· 0 citations
Few-shot classification (FSC) aims to emulate the human ability to rapidly learn new concepts from a handful of examples. Large Vision-Language Models (LVLMs), with their rich prior knowledge and powerful visio-linguistic understanding capabilities, are emerging as a highly promising paradigm for FSC. This paper invest...
Chuan-Yi Zhang, Fan Liu, Yi Xu et al.· IEEE Transactions on Image P...· 0 citations
Activation-Weighted Adaptive REtention (AWARe) is a fine-tuning method that mitigates catastrophic forgetting by dynamically controlling parameter updates based on activation patterns, and operates without modifying model architectures, ensuring compatibility with existing inference engines.
Jun-Yong Liao, Jinfan Lv, Guoming Wang et al.· 1 citation
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