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Instruction-guided visual modulation with iGVLM unlocks more fine-grained reasoning in LVLMs, outperforming static vision encoders by dynamically adapting visual representations to the specific textual task.
Image-text models can achieve superior performance by fusing modalities during training only, then discarding the fusion module at inference for efficiency.
