  A hallmark of the deep learning era for computer vision is the successful use
of large-scale labeled datasets to train feature representations for tasks
ranging from object recognition and semantic segmentation to optical flow
estimation and novel view synthesis of 3D scenes. In this work, we aim to learn
dense discriminative object representations for low-shot category recognition
without requiring any category labels. To this end, we propose Deep Object
Patch Encodings (DOPE), which can be trained from multiple views of object
instances without any category or semantic object part labels. To train DOPE,
we assume access to sparse depths, foreground masks and known cameras, to
obtain pixel-level correspondences between views of an object, and use this to
formulate a self-supervised learning task to learn discriminative object
patches. We find that DOPE can directly be used for low-shot classification of
novel categories using local-part matching, and is competitive with and
outperforms supervised and self-supervised learning baselines. Code and data
available at https://github.com/rehg-lab/dope_selfsup.
