DEX-AR: A Dynamic Explainability Method for Autoregressive Vision-Language Models | ScienceToStartup | ScienceToStartup

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References (41)

[1]

Token Activation Map to Visually Explain Multimodal LLMs

2025Yi Li, Hualiang Wang et al.

[2]

PaliGemma: A versatile 3B VLM for transfer

2024L. Beyer, A. Steiner et al.

[3]

Interpreting the Second-Order Effects of Neurons in CLIP

Founder's Pitch

"DEX-AR provides dynamic explainability for autoregressive vision-language models, generating heatmaps to highlight crucial image regions for textual responses, enabling better understanding and trust in VLM decisions."

Vision-Language Models Explainability•Score: 7•View PDF ↗

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DEX-AR: A Dynamic Explainability Method for Autoregressive Vision-Language Models

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References (41)

Founder's Pitch

"DEX-AR provides dynamic explainability for autoregressive vision-language models, generating heatmaps to highlight crucial image regions for textual responses, enabling better understanding and trust in VLM decisions."

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DEX-AR: A Dynamic Explainability Method for Autoregressive Vision-Language Models

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Founder's Pitch

"DEX-AR provides dynamic explainability for autoregressive vision-language models, generating heatmaps to highlight crucial image regions for textual responses, enabling better understanding and trust in VLM decisions."

Commercial Viability Breakdown

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