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ARXIV:2603.26646 · EGOCENTRIC VISION GROUNDING · SUBMITTED 30 MAR · 22:18 UTC · FRESHNESS STALE
ARXIV:2603.26646EGOCENTRIC VISION GROUNDINGSUBMITTED 30 MAR · 22:18 UTCFRESHNESS STALELing Li · Bowen Liu · Zinuo Zhan · Peng Jie · Jianhui Zhong · Kenglun Chang · +1 at arXiv
A new dataset and framework for egocentric visual grounding that uses hand pointing and language to resolve ambiguity, significantly improving agent comprehension of physical intents.
Opportunity summary
Pain A new dataset and framework for egocentric visual grounding that uses hand pointing and language to resolve ambiguity, significantly improving agent comprehension of physical intents.
Evidence 139 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A new dataset and framework for egocentric visual grounding that uses hand pointing and language to resolve ambiguity, significantly improving agent comprehension of physical intents. In natural egocentric engagements, hand-pointing combined with speech forms…
Traditional Visual Grounding (VG) predominantly relies on textual descriptions to localize objects, a paradigm that inherently struggles with linguistic ambiguity and often ignores non-verbal deictic cues prevalent in real-world interactions. In natural egocentric engagements,…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments demonstrate that SV-CoT achieves an $\textbf{11.7\%}$ absolute improvement over existing methods, effectively mitigating semantic ambiguity and advancing the capability of agents to…
Egocentric Vision Grounding moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A new dataset and framework for egocentric visual grounding that uses hand pointing and language to resolve ambiguity, significantly improving agent comprehension of physical intents.
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10.48550/arXiv.2603.26646A new dataset and framework for egocentric visual grounding that uses hand pointing and language to resolve ambiguity, significantly improving agent comprehension of physical intents.
Abstract
Traditional Visual Grounding (VG) predominantly relies on textual descriptions to localize objects, a paradigm that inherently struggles with linguistic ambiguity and often ignores non-verbal deictic cues prevalent in real-world interactions. In natural egocentric engagements, hand-pointing combined with speech forms the most intuitive referring mechanism. To bridge this gap, we introduce EgoPoint-Ground, the first large-scale multimodal dataset dedicated to egocentric deictic visual grounding. Comprising over \textbf{15k} interactive samples in complex scenes, the dataset provides rich, multi-grained annotations including hand-target bounding box pairs and dense semantic captions. We establish a comprehensive benchmark for hand-pointing referring expression resolution, evaluating a wide spectrum of mainstream Multimodal Large Language Models (MLLMs) and state-of-the-art VG architectures. Furthermore, we propose SV-CoT, a novel baseline framework that reformulates grounding as a structured inference process, synergizing gestural and linguistic cues through a Visual Chain-of-Thought paradigm. Extensive experiments demonstrate that SV-CoT achieves an $\textbf{11.7\%}$ absolute improvement over existing methods, effectively mitigating semantic ambiguity and advancing the capability of agents to comprehend multimodal physical intents. The dataset and code will be made publicly available.
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Proof status
unverified139 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
A new dataset and framework for egocentric visual grounding that uses hand pointing and language to resolve ambiguity, significantly improving agent comprehension of physical intents. In natural egocentric engagements, hand-pointing combined with speech forms the most intuitive...
METHOD
Traditional Visual Grounding (VG) predominantly relies on textual descriptions to localize objects, a paradigm that inherently struggles with linguistic ambiguity and often ignores non-verbal deictic cues prevalent in real-world interactions. In natural egocentric engagements, h...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments demonstrate that SV-CoT achieves an $\textbf{11.7\%}$ absolute improvement over existing methods, effectively mitigating semantic ambiguity and advancing the capability of agents to...
WHY NOW
Egocentric Vision Grounding moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
we introduce EgoPoint-Ground, the first large-scale multimodal dataset dedicated to egocentric deictic visual grounding.
The abstract explicitly states this and the dataset description section reinforces it.
partial
Comprising over 15k interactive samples in complex scenes, the dataset provides rich, multi-grained annotations including hand-target bounding box pairs and dense semantic captions.
The abstract provides the specific number of samples and the dataset description section confirms its scale.
partial
Extensive experiments demonstrate that SV-CoT achieves an 11.7% absolute improvement over existing methods, effectively mitigating semantic ambiguity and advancing the capability of agents to comprehend multimodal physical intents.
The abstract explicitly states this improvement percentage and the results table shows SV-CoT outperforming other methods.
partial
Furthermore, we propose SV-CoT, a novel baseline framework that reformulates grounding as a structured inference process, synergizing gestural and linguistic cues through a Visual Chain-of-Thought paradigm.
The abstract describes the SV-CoT framework and its approach, and the architecture overview visually supports this.
partial
Traditional Visual Grounding (VG) predominantly relies on textual descriptions to localize objects, a paradigm that inherently struggles with linguistic ambiguity and often ignores non-verbal deictic cues prevalent in real-world interactions.
The abstract clearly outlines the limitations of traditional VG methods.
partial
Comprising over 15k interactive samples in complex scenes, the dataset provides rich, multi-grained annotations including hand-target bounding box pairs and dense semantic captions.
The abstract mentions these annotations, and the dataset description section elaborates on the annotation process.
partial
The egocentric hand bounding box B2D hand is aligned into discrete spatial anchorsTpos. Zero-shot grounding is reformulated as a latent reasoni
The architectural overview of SV-CoT visually depicts these steps and the accompanying text explains the process.
partial
we introduce EgoPoint-Ground, the first large-scale multimodal dataset dedicated to egocentric deictic visual grounding.
The abstract explicitly states this, and the dataset description section reinforces it by calling it the 'first high-fidelity and high-complexity egocentric benchmark'.
partial
Comprising over 15k interactive samples in complex scenes, the dataset provides rich, multi-grained annotations including hand-target bounding box pairs and dense semantic captions.
The abstract provides the specific number of samples, and the dataset description section confirms the scale.
partial
Extensive experiments demonstrate that SV-CoT achieves an 11.7% absolute improvement over existing methods, effectively mitigating semantic ambiguity and advancing the capability of agents to comprehend multimodal physical intents.
The abstract explicitly states this quantitative improvement, and the results table shows a significant performance gain for the proposed method.
partial
Furthermore, we propose SV-CoT, a novel baseline framework that reformulates grounding as a structured inference process, synergizing gestural and linguistic cues through a Visual Chain-of-Thought paradigm.
The abstract describes the proposed method's approach, and the architecture overview in Figure 6 visually supports this description.
partial
Traditional Visual Grounding (VG) predominantly relies on textual descriptions to localize objects, a paradigm that inherently struggles with linguistic ambiguity and often ignores non-verbal deictic cues prevalent in real-world interactions.
The abstract clearly outlines the limitations of traditional VG methods.
partial
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A new dataset and framework for egocentric visual grounding that uses hand pointing and language to resolve ambiguity, significantly improving agent comprehension of physical intents.
Segment
Egocentric Vision Grounding
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7.0/10 public viability
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Evidence coverage
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139 refs / 3 sources / 50% coverage
stale
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Evidence
139 references, 3 sources, 50% evidence coverage.
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Write integration checklist from prototype path and target workflow.
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