An inference-time technique that uses token activation dynamics to adaptively restrict text attention to important visual tokens, improving VLM accuracy on VQA, grounding, counting, OCR, and hallucination benchmarks.
InstructBLIP: Towards general-purpose vision- language models with instruction tuning
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Aligning What Vision-Language Models See and Perceive with Adaptive Information Flow
An inference-time technique that uses token activation dynamics to adaptively restrict text attention to important visual tokens, improving VLM accuracy on VQA, grounding, counting, OCR, and hallucination benchmarks.