Data-Scarce Animal Face Alignment via Bi-Directional Cross-Species Knowledge Transfer

Animal face alignment is challenging due to large intra- and inter-species variations and a scarcity of labeled data. Existing studies circumvent this problem by directly finetuning a human face alignment model or focusing on animal-specific face alignment (e.g., horse, sheep). In this paper, we propose Cross-Species Knowledge Transfer, Meta-CSKT, for animal face alignment, which consists of a base network and an adaptation network. Two networks continuously complement each other through the bi-directional cross-species knowledge transfer. This is motivated by observing knowledge sharing among animals. Meta-CSKT uses a circuit feedback mechanism to improve the base network with the cognitive differences of the adaptation network between few-shot labeled and large-scale unlabeled data. In addition, we propose a positive example mining method to identify positives, semi-hard positives, and hard negatives in unlabeled data to mitigate the scarcity of labeled data and facilitate Meta-CSKT learning. Experiments show that Meta-CSKT outperforms state-of-the-art methods by a large margin on the horse facial keypoint dataset and Japanese Macaque Species dataset, while achieving comparable results to state-of-the-art methods on large-scale labeled AnimalWeb (e.g., 18K), using only a few labeled images (e.g., 40)1.