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- W4312259544 abstract "Self-supervised, category-agnostic segmentation of real-world images is a challenging open problem in computer vision. Here, we show how to learn static grouping priors from motion self-supervision by building on the cognitive science concept of a Spelke Object: a set of physical stuff that moves together. We introduce the Excitatory-Inhibitory Segment Extraction Network (EISEN), which learns to extract pairwise affinity graphs for static scenes from motion-based training signals. EISEN then produces segments from affinities using a novel graph propagation and competition network. During training, objects that undergo correlated motion (such as robot arms and the objects they move) are decoupled by a bootstrapping process: EISEN explains away the motion of objects it has already learned to segment. We show that EISEN achieves a substantial improvement in the state of the art for self-supervised image segmentation on challenging synthetic and real-world robotics datasets." @default.
- W4312259544 created "2023-01-04" @default.
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- W4312259544 date "2022-01-01" @default.
- W4312259544 modified "2023-10-04" @default.
- W4312259544 title "Unsupervised Segmentation in Real-World Images via Spelke Object Inference" @default.
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- W4312259544 doi "https://doi.org/10.1007/978-3-031-19818-2_41" @default.
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