Motion, measured via optical flow, provides a powerful cue to discover and
learn objects in images and videos. However, compared to using appearance, it
has some blind spots, such as the fact that objects become invisible if they do
not move. In this work, we propose an approach that combines the strengths of
motion-based and appearance-based segmentation. We propose to supervise an
image segmentation network with the pretext task of predicting regions that are
likely to contain simple motion patterns, and thus likely to correspond to
objects. As the model only uses a single image as input, we can apply it in two
settings: unsupervised video segmentation, and unsupervised image segmentation.
We achieve state-of-the-art results for videos, and demonstrate the viability
of our approach on still images containing novel objects. Additionally we
experiment with different motion models and optical flow backbones and find the
method to be robust to these change. Project page and code available at
this https URL

本文提出一种融合基于运动和外观的分割方法，利用预测简单运动模式区域对图像分割网络进行监督，使其具备检测和学习静止物体的能力。通过实验发现该方法不仅在无监督视频分割领域表现出色，而且能够适用于包含新颖物体的静态图像分割领域，同时还能够处理多种运动模型和光流基准的影响。