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This paper presents a semi-supervised learning framework to train a keypointdetector using multiview image streams given the limited labeled data(typically $<$4\%). We leverage the complementary relationship betweenmultiview geometry and visual tracking to provide three types of supervisionarysignals to utilize the unlabeled data: (1) keypoint detection in one view canbe supervised by other views via the epipolar geometry; (2) a keypoint movessmoothly over time where its optical flow can be used to temporally superviseconsecutive image frames to each other; (3) visible keypoint in one view islikely to be visible in the adjacent view. We integrate these three signals ina differentiable fashion to design a new end-to-end neural network composed ofthree pathways. This design allows us to extensively use the unlabeled data totrain the keypoint detector. We show that our approach outperforms existingdetectors including DeepLabCut tailored to the keypoint detection of non-humanspecies such as monkeys, dogs, and mice.

Multiview Supervision By Registration