Motion-supervised Co-Part Segmentation

Understand

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.