The Many Senses of Visual Similarity: A Text-Prompted Image Perceptual Metric
Human visual similarity judgments are context-dependent. For example, two images may be similar in shape but distinct in color. Existing perceptual similarity metrics, however, collapse these nuances into a single scalar value, offering no mechanism to condition on specific aspects. To bridge this gap, we introduce a large-scale dataset of human similarity judgments over image triplets, where each triplet is annotated across multiple, free-form semantic aspects of similarity. Benchmarking a broad range of frontier vision-language models (VLMs) reveals a considerable performance gap compared to
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- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “The Many Senses of Visual Similarity: A Text-Prompted Image ” ≈ “Tongyi-MAI/Z-Image-Turbo””
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Sheng-Yu Wang →
“The Many Senses of Visual Similarity: A Text-Prompted Image Perceptual Metric”
- LinkedLinked via arxiv author · 85%Yotam Nitzan →
“The Many Senses of Visual Similarity: A Text-Prompted Image Perceptual Metric”
- LinkedLinked via arxiv author · 85%Aaron Hertzmann →
“The Many Senses of Visual Similarity: A Text-Prompted Image Perceptual Metric”
- LinkedLinked via arxiv author · 85%Jun-Yan Zhu →
“The Many Senses of Visual Similarity: A Text-Prompted Image Perceptual Metric”
- LinkedLinked via arxiv author · 85%Eli Shechtman →
“The Many Senses of Visual Similarity: A Text-Prompted Image Perceptual Metric”