AP-10K: A Benchmark for Animal Pose Estimation in the Wild (2108.12617v2)

Abstract: Accurate animal pose estimation is an essential step towards understanding animal behavior, and can potentially benefit many downstream applications, such as wildlife conservation. Previous works only focus on specific animals while ignoring the diversity of animal species, limiting the generalization ability. In this paper, we propose AP-10K, the first large-scale benchmark for mammal animal pose estimation, to facilitate the research in animal pose estimation. AP-10K consists of 10,015 images collected and filtered from 23 animal families and 54 species following the taxonomic rank and high-quality keypoint annotations labeled and checked manually. Based on AP-10K, we benchmark representative pose estimation models on the following three tracks: (1) supervised learning for animal pose estimation, (2) cross-domain transfer learning from human pose estimation to animal pose estimation, and (3) intra- and inter-family domain generalization for unseen animals. The experimental results provide sound empirical evidence on the superiority of learning from diverse animals species in terms of both accuracy and generalization ability. It opens new directions for facilitating future research in animal pose estimation. AP-10k is publicly available at https://github.com/AlexTheBad/AP10K.

• The paper introduces AP-10K, demonstrating a significant advancement in animal pose estimation through its diverse, taxonomically-structured dataset with precise keypoint annotations.
• It benchmarks three experimental tracks—supervised learning, cross-domain transfer, and domain generalization—revealing robust improvements in accuracy and model convergence.
• The results indicate that leveraging dataset diversity and novel transfer learning approaches can enhance model performance, paving the way for more generalized and ecologically relevant pose estimation methods.

A Benchmark for Mammal Pose Estimation: Introduction and Implications

The paper "AP-10K: A Benchmark for Animal Pose Estimation in the Wild" delineates the creation and implementation of a novel benchmark dataset specifically designed for mammal animal pose estimation, termed AP-10K. This dataset addresses the limitations of existing animal pose benchmarks, which have predominantly focused on specific species, thus restricting broader applicability and generalization. By introducing a dataset consisting of over 10,015 images across 23 mammal families and 54 species, AP-10K represents a significant expansion in the diversity and complexity of available data for this field.

Dataset Creation and Structure

The AP-10K dataset is uniquely organized following a taxonomic rank, facilitating research into both specialized pose estimation tasks and broader taxonomic studies. Two major components characterize the dataset: (1) it contains manually annotated high-quality keypoints across a wide array of species, offering a strong foundation for supervised learning models, and (2) it includes a substantial collection of unlabeled images, enabling advancements in semi-supervised and self-supervised learning methods. The latter component is especially pertinent for extending pose estimation models to rare species with limited labeled samples.

Experimental Tracks and Evaluation

The authors benchmark existing pose estimation models on three principal tracks:

1. Supervised Learning: Evaluates the performance of key human pose estimation models adjusted for animal datasets. The paper underscores the value of diverse species training data, showing improved model performance both in accuracy and generalization when expanded training sets are employed.
2. Cross-Domain Transfer Learning: Investigates the impacts of transfer learning from human pose estimation models to animal pose tasks. The transferability of pre-trained models was analyzed to assess if such an approach accelerates convergence and enhances performance on novel animal data, especially when training data from animal sources are sparse.
3. Domain Generalization: Explores the intra- and inter-family generalization capabilities of pose estimation models, analyzing their ability to extrapolate from specific species within a family to new, unseen species in similar or different taxa. The results reveal encouraging generalization within closely related families, indicating potential biological similarities that can be leveraged for predicting unseen species’ poses.

Implications for Future Research

The findings from the AP-10K benchmark highlight several research directions and practical implications:

• Enhanced Generalization through Diversity: The incorporation of a wide array of species not only challenges current models but also enhances their applicability across different biological contexts. Future research could leverage this diversity to develop more robust, generalized algorithms capable of wildlife conservation tasks and behavior studies.
• Long-tail Distribution Handling: The inherent long-tail distribution of species within the dataset poses challenges similar to real-world ecological surveys, inviting the development of niche-specific strategies that can effectively deal with rare species detection and recognition.
• Advances in Transfer Learning: The varied results from transfer learning experiments suggest potential in refining domain adaptation techniques tailored to bridging gaps between human and animal model features. This could be vital for rapid deployment of pose estimation solutions in ecological and zoological studies.

Conclusion

The AP-10K benchmark offers a pivotal resource for animal pose estimation, addressing past dataset limitations in scope and diversity. Through its comprehensive structure and extensive empirical evaluations, it lays the groundwork for significant advancements in computational ecology, automated wildlife monitoring, and the understanding of animal behaviors. Its public availability will likely catalyze innovation and cross-institutional collaborations, ultimately advancing both theoretical and practical efforts in computer vision applications for zoology.