超越静态裁剪：层适应的视觉定位与解码增强
Beyond Static Cropping: Layer-Adaptive Visual Localization and Decoding Enhancement
AI总结：
本文提出LASER方法，通过动态层适应提升视觉定位和解码精度，解决静态裁剪在复杂推理任务中的局限性。
摘要：
大型视觉-语言模型（LVLMs）通过将视觉块对齐到文本嵌入空间而迅速发展，但固定的视觉-token预算迫使图像被调整到统一的预训练分辨率，常常导致细节丢失并因过度依赖语言先验而产生幻觉。最近的注意力引导增强（例如裁剪或区域聚焦注意力分配）缓解了这一问题，但通常依赖于静态的
英文摘要：
Large Vision-Language Models (LVLMs) have advanced rapidly by aligning visual patches with the text embedding space, but a fixed visual-token budget forces images to be resized to a uniform pretraining resolution, often erasing fine-grained details and causing hallucinations via over-reliance on language priors. Recent attention-guided enhancement (e.g., cropping or region-focused attention allocation) alleviates this, yet it commonly hinges on a static "magic layer" empirically chosen on simple recognition benchmarks and thus may not transfer to complex reasoning tasks. In contrast to this static assumption, we propose a dynamic perspective on visual grounding. Through a layer-wise sensitivity analysis, we demonstrate that visual grounding is a dynamic process: while simple object recognition tasks rely on middle layers, complex visual search and reasoning tasks require visual information to be reactivated at deeper layers. Based on this observation, we introduce Visual Activation by Query (VAQ), a metric that identifies the layer whose attention map is most relevant to query-specific visual grounding by measuring attention sensitivity to the input query. Building on VAQ, we further propose LASER (Layer-adaptive Attention-guided Selective visual and decoding Enhancement for Reasoning), a training-free inference procedure that adaptively selects task-appropriate layers for visual localization and question answering. Experiments across diverse VQA benchmarks show that LASER significantly improves VQA accuracy across tasks with varying levels of complexity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
