Do Vision-Language Models Agree on the Affective Qualities of Shape? A Cross-Model Audit for Generative Design Interfaces
Generative design interfaces increasingly expose semantic controls that let users steer output with concepts such as "more elegant" or "more minimalist," typically encoded by a vision-language model (VLM). A practical question is whether state-of-the-art VLMs represent objects consistently in terms of the same concept. We audit 6 VLMs by ranking untextured 3D objects along Kansei adjective pairs, where Kansei describes affective impressions of product form, with each axis defined as the difference between the text representations of its two poles. Geometric pairs serve as positive controls, an
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- FuzzySimilar title/name (fuzzy) · 59%VioletVision-3B →
“Fuzzy title match (0.73): “Do Vision-Language Models Agree on the Affective Qualities o” ≈ “VioletVision-3B””
- PossiblePossibly related (embedding) · 53%Large language models do not have emotions - Nature →
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- PossiblePossibly related (embedding) · 50%By modeling visual saliency, AI improves ratings of artistic product designs - Tech Xplore →
- PossiblePossibly related (embedding) · 48%Understanding large language models demands distinguishing human projection from machine cognition - Nature →
- FuzzySimilar title/name (fuzzy) · 84%GoogleCloudPlatform/generative-ai →
“Fuzzy title match (0.92): “Do Vision-Language Models Agree on the Affective Qualities o” ≈ “GoogleCloudPlatform/generative-ai””
- FuzzySimilar title/name (fuzzy) · 84%pytorch/vision →
“Fuzzy title match (0.92): “Do Vision-Language Models Agree on the Affective Qualities o” ≈ “pytorch/vision””
- FuzzySimilar title/name (fuzzy) · 59%steven2358/awesome-generative-ai →
“Fuzzy title match (0.73): “Do Vision-Language Models Agree on the Affective Qualities o” ≈ “steven2358/awesome-generative-ai””