Monkey Transfer Learning Can Improve Human Pose Estimation (2412.15966v1)

Abstract: In this study, we investigated whether transfer learning from macaque monkeys could improve human pose estimation. Current state-of-the-art pose estimation techniques, often employing deep neural networks, can match human annotation in non-clinical datasets. However, they underperform in novel situations, limiting their generalisability to clinical populations with pathological movement patterns. Clinical datasets are not widely available for AI training due to ethical challenges and a lack of data collection. We observe that data from other species may be able to bridge this gap by exposing the network to a broader range of motion cues. We found that utilising data from other species and undertaking transfer learning improved human pose estimation in terms of precision and recall compared to the benchmark, which was trained on humans only. Compared to the benchmark, fewer human training examples were needed for the transfer learning approach (1,000 vs 19,185). These results suggest that macaque pose estimation can improve human pose estimation in clinical situations. Future work should further explore the utility of pose estimation trained with monkey data in clinical populations.

• The paper proposes leveraging transfer learning from macaque monkeys to improve human pose estimation, particularly addressing the challenge of limited clinical human data.
• Using macaque data for pre-training allows the model to be fine-tuned effectively with significantly fewer human examples compared to training solely on human data.
• Results show that the monkey transfer learning approach yields improved metrics like precision and recall, demonstrating its potential to enhance pose estimation for populations with pathological movement patterns.

Overview of "Monkey Transfer Learning Can Improve Human Pose Estimation"

The paper "Monkey Transfer Learning Can Improve Human Pose Estimation" presents an innovative approach to improving human pose estimation by leveraging transfer learning techniques using data from macaque monkeys. This paper addresses a significant challenge in machine learning: the scarcity of labeled data for training models on specific tasks, particularly in the context of clinical populations with pathological movement patterns.

Problem Context and Proposition

Human pose estimation has wide-ranging applications across sectors such as entertainment, sports, and clinical rehabilitation. Existing state-of-the-art methods using deep learning can achieve performance levels comparable to human annotation in non-clinical datasets. However, these methods often falter when applied to clinical settings due to the novel pathological movement patterns encountered there, coupled with a lack of diverse training data. Importantly, clinical datasets are not abundantly available due to ethical constraints and challenges in data collection.

The authors propose an intriguing solution: utilizing transfer learning from species with more diverse movements—specifically, macaque monkeys. The rationale is that the movements and keypoints in macaque data, despite species differences, could enrich a model's capability to estimate human poses by exposing it to a wider range of motion cues. Such an approach could reduce the dependency on large human-specific datasets.

Methodological Approach

The paper employs a transfer learning methodology by fine-tuning a macaque monkey pose estimation network using human data. The process involves several key steps:

1. Macaque Network Baseline: A macaque pose estimation model was developed using DeepLabCut, trained on 14,697 images from a macaque dataset. This model serves as the foundation for the transfer learning process.
2. Human Network Benchmarking: The benchmark human model used for comparison was trained on the MPII dataset using ResNet architecture. The human model required a substantially larger volume of human examples (19,185 in total) compared to the transfer learning model (1,000 examples).
3. Transfer Learning Model: Fine-tuning the macaque model with human examples allowed the authors to evaluate improvements in human pose estimation metrics such as precision and recall.

Results and Analysis

The paper's results indicate notable improvements in precision, recall, and F1 scores with the transfer learning approach compared to the macaque baseline and the human-only benchmark. Noteworthy are the implications these improvements have, particularly reflected in Recall (0.94 for TL vs. 0.83 for the benchmark) and Precision (0.72 for TL vs. 0.69 for the benchmark).

The data shows that the transfer learning approach provides not only effective keypoint localization but does so with significantly fewer human data points necessary for training. This underscores the efficiency of learning from models trained on behaviorally diverse animal datasets before fine-tuning with task-specific human data.

Implications and Future Directions

The authors suggest that, despite differences between human and monkey appearance, the skeletal similarities and diverse motions of monkeys serve as a beneficial pre-training dataset. The broader implications include a potential paradigm shift in how clinical pose estimation models are trained. Integrating transfer learning from animal models into training pipelines could help mitigate the challenges associated with clinical data scarcity.

Future work could explore the use of other animal data sets and refining transfer learning techniques to optimize the feature identification process further. This could involve more advanced methods, such as freezing specific layers or employing more granular transfer learning strategies, which were limitations in the current paper due to software constraints.

The findings hold promise for advancements in clinical applications of pose estimation, particularly in enhancing diagnostic and therapeutic systems for population groups with pathological movements. Broadening the scope to include data from more species and incorporating state-of-the-art deep learning techniques could pave the way for improved, accessible, and universally applicable movement analysis tools.