AI RESEARCH
Robust Grounding with MLLMs against Occlusion and Small Objects via Language-guided Semantic Cues
arXiv CS.CV
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ArXi:2604.24036v1 Announce Type: new While Multimodal Large Language Models (MLLMs) have enhanced grounding capabilities in general scenes, their robustness in crowded scenes remains underexplored. Crowded scenes entail visual challenges (i.e., occlusion and small objects), which impair object semantics and degrade grounding performance. In contrast, language expressions are immune to such degradation and preserve object semantics. In light of these observations, we propose a novel method that overcomes such constraints by leveraging Language-Guided Semantic Cues (LGSCs