Learning New Dimensions of Human Visual Similarity using Synthetic Data
Name
fu-fus-meng-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
20.08 MB
Format
Adobe PDF
Checksum (MD5)
33f1805c59d67e1241a7aa790832a1b8
Author(s)
Fu, Stephanie
Advisor(s)
Isola, Phillip
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
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 poses, and semantic content. In this thesis, 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 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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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