Focus, Don't Prune: Identifying Instruction-Relevant Regions for Information-Rich Image Understanding
Large Vision-Language Models (LVLMs) have shown strong performance across various multimodal tasks by leveraging the reasoning capabilities of Large Language Models (LLMs). However, processing visually complex and information-rich images, such as infographics or document layouts, requires these models to generate a large number of visual tokens, leading to significant computational overhead. To address this, we propose PinPoint, a novel two-stage framework that first identifies instruction-relevant image regions and then refines them to extract fine-grained visual features for improved reasoning and efficiency. Central to our approach is the Instruction-Region Alignment, which localizes r...
| Authors | Mincheol Kwon, Minseung Lee, Seonga Choi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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