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About this paper
Zhaodong Wu, Haochen Xue, Qi Cao, Wenqi Mo, Yu Pei, Wenqi Xu, Jionglong Su, Yang Liu
ConFoThinking, a new framework, improves the reliability of region-of-interest localization for multimodal large language models in visual question answering tasks. It achieves state-of-the-art results across several VQA benchmarks by stabilizing internal attention mechanisms and leveraging semantically guided visual cues.
Key insights
- Direct autoregressive decoding of continuous bounding-box coordinates is inherently fragile and prone to errors, even when internal model perception is correct.
- Condensing attention into a specific, designated layer, rather than averaging across multiple layers, yields more stable and precise where-to-look signals for ROI extraction.
- Disentangling the semantic 'what to look for' (via concise focus cues) from the 'where to look' (via stable attention heatmaps) significantly enhances the reliability of ROI mining.
Summary by alphaXiv.
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