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CRAM achieves a breakthrough in parameter efficiency and task retention by dynamically allocating resources based on task-specific needs, avoiding the pitfalls of both shared updates and isolated expansions.
Format-aware task prototypes in ProtoAda prevent inter-task interference, leading to superior performance in multimodal instruction tuning.
Decomposing CLIP-based classification into attribute extraction and aggregation reveals a new path to mitigating catastrophic forgetting in class-incremental learning.
Stop wrestling with monolithic MLLM codebases: Prism offers a plug-and-play framework for multimodal continual learning research.
