Papers with parameter-efficient fine-tuning technique

1 papers
Task-Aware Resolution Optimization for Visual Large Language Models (2025.emnlp-main)

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Challenge: Existing visual large language models pre-assume a fixed resolution for downstream tasks, leading to sub-optimal performance.
Approach: They propose a formula to determine the optimal resolution for a given vision-language task . they then propose 'parameter-efficient' fine-tuning technique to extend the visual input resolution .
Outcome: The proposed method is based on rigorous experiments on vision-language tasks.

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