Recent co-part segmentation methods mostly operate in a supervised learning setting, which requires a large amount of annotated data for training. To overcome this limitation, we propose a self-supervised deep learning method for co-part segmentation. Differently from previous works, our approach develops the idea that motion information inferred from videos can be leveraged to discover meaningful object parts. To this end, our method relies on pairs of frames sampled from the same video. The network learns to predict part segments together with a representation of the motion between two frames, which permits reconstruction of the target image. Through extensive experimental evaluation on publicly available video sequences we demonstrate that our approach can produce improved segmentation maps with respect to previous self-supervised co-part segmentation approaches.

中文翻译：

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运动监督的共同部分分割

最近的 co-part 分割方法主要在监督学习环境中运行，这需要大量带注释的数据进行训练。为了克服这个限制，我们提出了一种用于共同部分分割的自监督深度学习方法。与以前的工作不同，我们的方法提出了这样一种想法，即可以利用从视频中推断出的运动信息来发现有意义的对象部分。为此，我们的方法依赖于从同一视频中采样的帧对。网络学习预测部分片段以及两帧之间的运动表示，这允许重建目标图像。