AI RESEARCH
GroundingME: Exposing the Visual Grounding Gap in MLLMs through Multi-Dimensional Evaluation
arXiv CS.CV
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ArXi:2512.17495v2 Announce Type: replace Visual grounding, localizing objects from natural language descriptions, represents a critical bridge between language and vision understanding. While multimodal large language models (MLLMs) achieve impressive scores on existing benchmarks, a fundamental question remains: can MLLMs truly visually ground with human-like sophistication, or are they merely pattern-matching on simplified datasets? Current benchmarks fail to capture real-world complexity where humans effortlessly navigate intricate references and recognize when grounding is impossible.