Papers with inter-task collaboration

2 papers
Enhancing Multimodal Continual Instruction Tuning with BranchLoRA (2025.acl-long)

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Challenge: Existing approaches to fine tune Multimodal Large Language Models (MLLMs) are prone to Catastrophic Forgetting (CF) existing approaches rely on the Mixture-of-Experts (MoE) LoRA framework to preserve previous instruction alignments.
Approach: They propose an asymmetric tuning-freezing mechanism to mitigate parameter inefficiency . branch-specific routers are introduced to ensure optimal branch distribution over time .
Outcome: The proposed framework outperforms existing frameworks on the latest MCIT benchmarks.
Task-Specific Information Decomposition for End-to-End Dense Video Captioning (2025.acl-long)

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Challenge: Existing methods for label assignment fail to ensure relevance of localization information to descriptions.
Approach: They propose a decomposed dense video captioning framework that derives localization and captioning queries from event queries, enabling task-specific representations while maintaining inter-task collaboration.
Outcome: Experiments on YouCook2 and ActivityNet Captions show that the proposed framework achieves state-of-the-art performance.

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