Unsupervised Segmentation in Real-World Images via Spelke Object Inference

• URL: http://arxiv.org/abs/2205.08515v1
• Date: Tue, 17 May 2022 17:39:24 GMT
• Title: Unsupervised Segmentation in Real-World Images via Spelke Object Inference
• Authors: Honglin Chen, Rahul Venkatesh, Yoni Friedman, Jiajun Wu, Joshua B. Tenenbaum, Daniel L. K. Yamins, Daniel M. Bear
• Abstract summary: Excitatory-Inhibitory Segment Extraction Network (EISEN) learns from optical flow estimates to extract pairwise affinity graphs for static scenes. We show that EISEN achieves a substantial improvement in the state of the art for self-supervised segmentation on challenging synthetic and real-world robotic image datasets.
• Score: 44.79376336842088
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised category-agnostic segmentation of real-world images into objects is a challenging open problem in computer vision. Here, we show how to learn static grouping priors from motion self-supervision, building on the cognitive science notion of Spelke Objects: groupings of stuff that move together. We introduce Excitatory-Inhibitory Segment Extraction Network (EISEN), which learns from optical flow estimates to extract pairwise affinity graphs for static scenes. EISEN then produces segments from affinities using a novel graph propagation and competition mechanism. Correlations between independent sources of motion (e.g. robot arms) and objects they move are resolved into separate segments through a bootstrapping training process. We show that EISEN achieves a substantial improvement in the state of the art for self-supervised segmentation on challenging synthetic and real-world robotic image datasets. We also present an ablation analysis illustrating the importance of each element of the EISEN architecture.

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