[Paper] VISion On Request: Enhanced VLLM efficiency with sparse, dynamically selected, vision-language interactions

Summary

VISOR introduces a new method to enhance Large Vision-Language Model (LVLM) efficiency by using sparse, dynamically selected vision-language interactions, moving away from the common practice of visual token reduction. This approach aims to reduce inference costs without discarding crucial visual information, thereby overcoming the performance bottlenecks faced by existing methods on complex tasks. By challenging the visual token reduction paradigm, VISOR seeks to improve LVLM performance, especially for tasks requiring fine-grained understanding and reasoning.

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