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ARXIV:2603.12606 · VISION-LANGUAGE GROUNDING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.12606VISION-LANGUAGE GROUNDINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
D-Negation enhances vision-language grounding models by introducing a dataset and learning framework focused on negation semantics.
Opportunity summary
Pain D-Negation enhances vision-language grounding models by introducing a dataset and learning framework focused on negation semantics.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
D-Negation enhances vision-language grounding models by introducing a dataset and learning framework focused on negation semantics. A key reason for this limitation is the lack of high-quality training data that explicitly captures discriminative negative…
Current vision-language detection and grounding models predominantly focus on prompts with positive semantics and often struggle to accurately interpret and ground complex expressions containing negative semantics. A key reason for this limitation is the…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. By fine-tuning fewer than 10 percent of the model parameters, our approach achieves improvements of up to 4.4 mAP and 5.7 mAP on positive…
Vision-Language Grounding moved forward this cycle; last verified April 2026. Public score 7.0/10.
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D-Negation enhances vision-language grounding models by introducing a dataset and learning framework focused on negation semantics.
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10.48550/arXiv.2603.12606D-Negation enhances vision-language grounding models by introducing a dataset and learning framework focused on negation semantics.
Abstract
Current vision-language detection and grounding models predominantly focus on prompts with positive semantics and often struggle to accurately interpret and ground complex expressions containing negative semantics. A key reason for this limitation is the lack of high-quality training data that explicitly captures discriminative negative samples and negation-aware language descriptions. To address this challenge, we introduce D-Negation, a new dataset that provides objects annotated with both positive and negative semantic descriptions. Building upon the observation that negation reasoning frequently appears in natural language, we further propose a grouped opposition-based learning framework that learns negation-aware representations from limited samples. Specifically, our method organizes opposing semantic descriptions from D-Negation into structured groups and formulates two complementary loss functions that encourage the model to reason about negation and semantic qualifiers. We integrate the proposed dataset and learning strategy into a state-of-the-art language-based grounding model. By fine-tuning fewer than 10 percent of the model parameters, our approach achieves improvements of up to 4.4 mAP and 5.7 mAP on positive and negative semantic evaluations, respectively. These results demonstrate that explicitly modeling negation semantics can substantially enhance the robustness and localization accuracy of vision-language grounding models.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Viability
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Dimensions overall score 7.0
PROBLEM
D-Negation enhances vision-language grounding models by introducing a dataset and learning framework focused on negation semantics. A key reason for this limitation is the lack of high-quality training data that explicitly captures discriminative negative samples and negation-aw...
METHOD
Current vision-language detection and grounding models predominantly focus on prompts with positive semantics and often struggle to accurately interpret and ground complex expressions containing negative semantics. A key reason for this limitation is the lack of high-quality tra...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. By fine-tuning fewer than 10 percent of the model parameters, our approach achieves improvements of up to 4.4 mAP and 5.7 mAP on positive and negative semantic evaluations, respectively.
WHY NOW
Vision-Language Grounding moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
D-Negation enhances vision-language grounding models by introducing a dataset and learning framework focused on negation semantics. A key reason for this limitation is the lack of high-quality training data that explicitly captures discriminative negative samples and negation-aware language descriptions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Current vision-language detection and grounding models predominantly focus on prompts with positive semantics and often struggle to accurately interpret and ground complex expressions containing negative semantics. A key reason for this limitation is the lack of high-quality training data that explicitly captures discriminative negative samples and negation-aware language descriptions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. By fine-tuning fewer than 10 percent of the model parameters, our approach achieves improvements of up to 4.4 mAP and 5.7 mAP on positive and negative semantic evaluations, respectively.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision-Language Grounding moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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D-Negation enhances vision-language grounding models by introducing a dataset and learning framework focused on negation semantics.
Segment
Vision-Language Grounding
Adoption evidence
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Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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confidence low
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Technical feasibility
partial
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ARTIFACTS
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DEFENSIBILITY
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