DRScaffold: Boosting Dense-Scene Reasoning in Lightweight Vision Language Models
Lightweight vision-language models perform competitively on standard benchmarks yet fail systematically in dense-scene reasoning, where multiple objects, attributes, and relations must be jointly grounded and resolved through multi-step inference. Such capability is critical for real-world applications where models must reliably interpret cluttered environments. Yet existing training signals provide no explicit grounding between reasoning steps and the underlying visual entities and relations, leaving lightweight models free to generate fluent but visually unanchored reasoning chains. To address this gap, we first introduce DRBench, a benchmark of 14,573 questions across 2,943 images, org...
| Authors | Xinrui Shi, Kai Liu, Ziqing Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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