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• EgoPoint-Ground is the first large-scale multimodal dataset dedicated to egocentric deictic visual grounding.technical 95%
• The EgoPoint-Ground dataset comprises over 15,000 interactive samples in complex scenes.technical 95%
• SV-CoT achieves an 11.7% absolute improvement over existing methods for hand-pointing referring expression resolution.result 95%
• SV-CoT reformulates grounding as a structured inference process, synergizing gestural and linguistic cues through a Visual Chain-of-Thought paradigm.method 90%
• Traditional Visual Grounding (VG) predominantly relies on textual descriptions and struggles with linguistic ambiguity and non-verbal deictic cues.limitation 90%
• The EgoPoint-Ground dataset includes annotations for hand-target bounding box pairs and dense semantic captions.technical 90%
• The SV-CoT framework aligns the egocentric hand bounding box into discrete spatial anchors and reformulates zero-shot grounding as a latent reasoning process.method 90%
• EgoPoint-Ground is the first large-scale multimodal dataset dedicated to egocentric deictic visual grounding.technical 95%
• The EgoPoint-Ground dataset comprises over 15,000 interactive samples in complex scenes.technical 95%
• SV-CoT achieves an 11.7% absolute improvement over existing methods for hand-pointing referring expression resolution.result 95%
• SV-CoT reformulates grounding as a structured inference process, synergizing gestural and linguistic cues through a Visual Chain-of-Thought paradigm.method 90%
• Traditional Visual Grounding (VG) predominantly relies on textual descriptions and struggles with linguistic ambiguity and non-verbal deictic cues.limitation 90%

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