  Multi-animal pose estimation is essential for studying animals' social
behaviors in neuroscience and neuroethology. Advanced approaches have been
proposed to support multi-animal estimation and achieve state-of-the-art
performance. However, these models rarely exploit unlabeled data during
training even though real world applications have exponentially more unlabeled
frames than labeled frames. Manually adding dense annotations for a large
number of images or videos is costly and labor-intensive, especially for
multiple instances. Given these deficiencies, we propose a novel
semi-supervised architecture for multi-animal pose estimation, leveraging the
abundant structures pervasive in unlabeled frames in behavior videos to enhance
training, which is critical for sparsely-labeled problems. The resulting
algorithm will provide superior multi-animal pose estimation results on three
animal experiments compared to the state-of-the-art baseline and exhibits more
predictive power in sparsely-labeled data regimes.
