# Monkey Transfer Learning Can Improve Human Pose Estimation

arXiv: https://arxiv.org/abs/2412.15966 · HTML: https://mlsanity.com/paper/2412.15966/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [Space-Time Representation of People Based on 3D Skeletal Data: A Review](https://mlsanity.com/paper/1601.01006/) (2016)
- [Understanding Deep Convolutional Networks through Gestalt Theory](https://mlsanity.com/paper/1810.08697/) (2018)
- [Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks](https://mlsanity.com/paper/2411.00627/) (2024)

## Abstract (arXiv)

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.