arXiv AI
Perceptual Reality Transformer: What Must an Illustration Preserve?
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arXiv AI
Jul 21
What Makes Linguistic Representations Good Models of High-Level Visual Perception in the Human Brain?
arXiv:2607. 16214v1 Announce Type: cross Abstract: Image descriptions represented with language models (LMs) predict human brain responses to naturalistic images in high-level visual regions, but the factors driving this predictivity remain unclear.
arXiv AI
Sep 15
Context-Dependent Affordance Reports in Vision-Language Models
arXiv:2603.04419v3 Announce Type: replace-cross Abstract: Vision-language models produce different object and use descriptions under different persona prompts, but low overlap alone does not identify...
arXiv Computer Vision
Sep 22
MMArt: A Multi-Perspective Multimodal Dataset for Visual Art Understanding
arXiv:2608.10706v3 Announce Type: replace Abstract: Recent vision-language models demonstrate impressive general visual understanding, yet their art interpretation remains shallow: they describe surf...
arXiv Computer Vision
4d ago
DARE to Mitigate Hallucination: Dual-path Auto-Regressive-aware Editing
arXiv:2609.36440v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have recently achieved remarkable progress across multimodal tasks, yet object hallucination remains a persistent...
arXiv AI
Sep 3
Blending Concepts: Benchmarking Visual Metaphor Generation in Text-to-Image Models
The paper introduces VMetaphor-Bench, a benchmark for evaluating visual metaphor generation in text-to-image models, comprising 1,500 curated metaphors across three levels and ten categories, each paired with two prompts of varying specificity. It proposes a hybrid evaluation framework using a multiple-choice question protocol and dimension-based scoring to assess metaphorical fidelity. Experiments on 11 T2I models show that even top proprietary models struggle with compositional structuring and cross-domain mapping, underscoring the need for further research in this area.
arXiv AI
Jun 15
Mirage Probes: How Vision Models Fake Visual Understanding
arXiv:2606. 13870v1 Announce Type: cross Abstract: Vision-language models (VLMs) can answer image-based questions confidently, and often correctly, even when no image is provided.