AI RESEARCH
KARL: Knowledge-Aware Reasoning and Reinforcement Learning for Knowledge-Intensive Visual Grounding
arXiv CS.CV
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ArXi:2503.12797v3 Announce Type: replace Knowledge-Intensive Visual Grounding (KVG) requires models to localize objects using fine-grained, domain-specific entity names rather than generic referring expressions. Although Multimodal Large Language Models (MLLMs) possess rich entity knowledge and strong generic grounding capabilities, they often fail to effectively utilize such knowledge when grounding specialized concepts, revealing a knowledge-grounding gap between internal knowledge and grounding predictions. To address this challenge, we propose a knowledge-aware.