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Cited by 108Domain Adaptation and Few-Shot LearningGenerative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data

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Fu, Stephanie, Tamir, Netanel, Sundaram, Shobhita, Chai, Lucy, Zhang, Richard, Dekel, Tali, Isola, Phillip·2023·Advances in Neural Information Processing Systems 36
DOI 10.52202/075280-2208
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Abstract

Current perceptual similarity metrics operate at the level of pixels and patches. These metrics compare images in terms of their low-level colors and textures, but fail to capture mid-level similarities and differences in image layout, object pose, and semantic content. In this paper, we develop a perceptual metric that assesses images holistically. Our first step is to collect a new dataset of human similarity judgments over image pairs that are alike in diverse ways. Critical to this dataset is that judgments are nearly automatic and shared by all observers. To achieve this we use recent text-to-image models to create synthetic pairs that are perturbed along various dimensions. We observe that popular perceptual metrics fall short of explaining our new data, and we introduce a new metric, DreamSim, tuned to better align with human perception. We analyze how our metric is affected by different visual attributes, and find that it focuses heavily on foreground objects and semantic content while also being sensitive to color and layout. Notably, despite being trained on synthetic data, our metric generalizes to real images, giving strong results on retrieval and reconstruction tasks. Furthermore, our metric outperforms both prior learned metrics and recent large vision models on these tasks.

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  • LPIPS lacks a semantic understanding of scenes and is more sensitive to color and texture.

    “Moreover, LPIPS lacks a semantic understanding of scenes, being more sensitive to color and texture.”

    From MTReD: 3D Reconstruction Dataset for Fly-over Videos of Maritime Domain (2025) · cites this paper as [20]

    The paper’s ‘Alignment with semantic and low-level metrics’ discussion reports that LPIPS aligns more strongly with RGB-color similarity than DreamSim, while DreamSim aligns better with foreground-object category similarity. Table 6 (supplement, around p. 21) supports that comparison, but it does not establish that LPIPS lacks semantic understanding altogether. The cited passages do not assess LPIPS’s sensitivity to texture. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Fu, S., Tamir, N., Sundaram, S., Chai, L., Zhang, R., Dekel, T., & Isola, P.
ORCID
Venue
Advances in Neural Information Processing Systems 36
Publisher
Neural Information Processing Systems Foundation, Inc. (NeurIPS)
Published
2023
Pages
50742-50768
ISSN
1049-5258 (ISSN-L)
DOI
10.52202/075280-2208
OpenAlex
W7133220105
Cited by
108
References
0
Citations by year
2026: 62 · 2025: 17 · 2024: 28 · 2022: 1
FWCI
18.98
Type
conference paper
Language
en
Open access
Licence
cc-by-nc-sa
Locations
  • neural information processing systems (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
synthetic data, generative models

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