Beyond Dominant Patches: Spatial Credit Redistribution For Grounded Vision-Language Models | ScienceToStartup | ScienceToStartup

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References (34)

[1]

Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering

2026Shuliang Liu, Songbo Yang et al.

[2]

CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Models

2026Neeraj Anand, Samyak Jha et al.

[3]

Understanding and Mitigating Hallucination in Large Vision-Language Models via Modular Attribution and Intervention

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"A practical solution to reduce hallucination in vision-language models through inference-time spatial credit redistribution."

Vision-Language Models•Score: 8•View PDF ↗

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Beyond Dominant Patches: Spatial Credit Redistribution For Grounded Vision-Language Models

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References (34)

Founder's Pitch

"A practical solution to reduce hallucination in vision-language models through inference-time spatial credit redistribution."

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Beyond Dominant Patches: Spatial Credit Redistribution For Grounded Vision-Language Models

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References (34)

Founder's Pitch

"A practical solution to reduce hallucination in vision-language models through inference-time spatial credit redistribution."

Commercial Viability Breakdown

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