论文标题
猜猜移动是什么：无监督的视频和图像分割通过预期运动
Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion
论文作者
论文摘要
通过光流量测量的运动为在图像和视频中发现和学习对象提供了强大的提示。但是，与使用外观相比，它具有一些盲点，例如，如果物体不移动，它们就会变得不可见。在这项工作中，我们提出了一种结合基于运动和基于外观分割的优势的方法。我们建议通过借口进行预测可能包含简单运动模式的区域的借口任务监督图像分割网络，因此可能与对象相对应。由于该模型仅使用单个图像作为输入，因此我们可以在两个设置中应用它：无监督的视频分割和无监督的图像分割。我们实现了视频的最新结果，并在包含新物体的静止图像上证明了我们的方法的可行性。此外，我们尝试了不同的运动模型和光流式骨架，并找到了对这些变化具有鲁棒性的方法。项目页面和代码可在https://www.robots.ox.ac.uk/~vgg/research/gwm上找到。
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 https://www.robots.ox.ac.uk/~vgg/research/gwm.
