Object Concepts Emerge from Motion
Object-centric visual representations are important for physical-world perception, but existing visual pretraining methods often capture semantic categories without preserving the identity and coherence of individual instances. We present a biologically inspired framework that learns object-centric representations for single images from raw videos. Our approach uses motion boundaries as a source of object-level grouping: off-the-shelf optical flow and clustering produce pseudo-instance masks, which supervise a single-image encoder with pixel-level pairwise metric learning. The framework requires neither human annotations nor camera calibration. We first obtain 195 million pseudo-labeled f...
| Authors | Boshi Li, Xiaohui Wang, Xiaoyang Wu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 September 2026 |
| Last updated at source | 9 October 2026 |
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