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.

利用动态自我监督学习来学习静态场景的成对亲和力图，然后通过一种新的图传播和竞争网络来产生分割，从而在挑战合成和现实世界机器人数据集上实现了自我监督图像分割的显着改进。