论文标题
通过Spelke对象推理，在现实世界中的无监督分割
Unsupervised Segmentation in Real-World Images via Spelke Object Inference
论文作者
论文摘要
在计算机视觉中，对现实世界图像的自我监督，类别不合时宜的分割是一个具有挑战性的开放问题。在这里，我们通过基于Spelke对象的认知科学概念来展示如何从运动自学的静态分组中学习静态分组：一组可以一起移动的物理内容。我们介绍了兴奋性抑制性段提取网络（EISEN），该网络学会从基于运动的训练信号中提取成对的亲和力图，以供静态场景。然后，艾森使用新颖的图表传播和竞争网络从亲和力产生细分市场。在训练过程中，进行相关运动的对象（例如机器人臂和移动的对象）被引导过程解耦：Eisen解释了它已经学会了细分的对象的运动。我们表明，艾森（Eisen）在挑战合成和现实世界的机器人数据集方面进行了自我监督的图像分割方面的最大改进。
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.
