Human-like Object Grouping in Self-supervised Vision Transformers
Vision foundation models trained with self-supervised objectives achieve strong performance across diverse tasks and exhibit emergent object segmentation properties. However, their alignment with human object perception remains poorly understood. Here, we introduce a behavioral benchmark in which participants make same/different object judgments for dot pairs on naturalistic scenes, scaling up a classical psychophysics paradigm to over 1000 trials. We test a diverse set of vision models using a simple readout from their representations to predict subjects' reaction times. We observe a steady improvement across model generations, with both architecture and training objective contributing t...
| Authors | Hossein Adeli, Seoyoung Ahn, Andrew Luo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 March 2026 |
| Last updated at source | 9 July 2026 |
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