  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.
