TransPose: Keypoint Localization via Transformer (2012.14214v5)

Abstract: While CNN-based models have made remarkable progress on human pose estimation, what spatial dependencies they capture to localize keypoints remains unclear. In this work, we propose a model called \textbf{TransPose}, which introduces Transformer for human pose estimation. The attention layers built in Transformer enable our model to capture long-range relationships efficiently and also can reveal what dependencies the predicted keypoints rely on. To predict keypoint heatmaps, the last attention layer acts as an aggregator, which collects contributions from image clues and forms maximum positions of keypoints. Such a heatmap-based localization approach via Transformer conforms to the principle of Activation Maximization~\cite{erhan2009visualizing}. And the revealed dependencies are image-specific and fine-grained, which also can provide evidence of how the model handles special cases, e.g., occlusion. The experiments show that TransPose achieves 75.8 AP and 75.0 AP on COCO validation and test-dev sets, while being more lightweight and faster than mainstream CNN architectures. The TransPose model also transfers very well on MPII benchmark, achieving superior performance on the test set when fine-tuned with small training costs. Code and pre-trained models are publicly available\footnote{\url{https://github.com/yangsenius/TransPose}}.

• The paper introduces TransPose, which integrates Transformer attention with a CNN backbone to enhance keypoint localization in human pose estimation.
• Experimental results show 75.8 AP on COCO and 75.0 AP on MPII with a 73% parameter reduction and 1.4× faster speed than HRNet-W48.
• The approach improves spatial dependency modeling, offering greater interpretability and a promising framework for future vision research.

TransPose: Keypoint Localization via Transformer

The paper "TransPose: Keypoint Localization via Transformer" presents an innovative approach to human pose estimation by integrating Transformer architectures into the prediction of keypoint heatmaps. This work addresses the limitations of Convolutional Neural Network (CNN) models, which previously dominated the field yet lacked clarity in explaining the spatial dependencies exploited for localizing keypoints.

Methodology

TransPose leverages the attention mechanism inherent in Transformers to capture long-range dependencies more effectively than traditional CNNs. The model architecture is composed of three main components: a CNN backbone for feature extraction, a Transformer Encoder to capture long-range interactions, and a head to predict keypoint heatmaps. The attention layers allow TransPose to explicitly model spatial relationships, which can be crucial for challenging scenarios such as occlusions.

The authors propose that the last attention layer functions as an aggregator, collecting contributions from various image regions, thus forming maximum positions in the keypoint heatmaps. This feature aligns with the principle of Activation Maximization, extending its interpretability to localization tasks by indicating image-specific dependencies.

Experimental Results

The results on the COCO and MPII datasets show TransPose achieving 75.8 AP on the COCO validation set and 75.0 AP on test-dev, while maintaining a lightweight architecture. Notably, TransPose exhibits a 73% reduction in parameters and is 1.4 times faster compared to HRNet-W48, marking a significant improvement in efficiency and speed. Furthermore, the model demonstrates strong transferability to the MPII benchmark, achieving high accuracy with minimal training costs.

Implications and Future Directions

TransPose offers a compelling alternative to traditional CNN-based pose estimation methods by enhancing interpretability and efficiency. Its ability to reveal image-specific and fine-grained dependencies can assist practitioners in understanding model predictions better. Moreover, the fine-tuning capabilities on MPII highlight the potential for large-scale pre-training in human pose estimation, suggesting future research directions focusing on Transformer-based models for various vision tasks.

The paper opens avenues for further exploration into the application of Transformers across other domains, potentially enhancing explainability and performance in complex vision systems. The findings encourage a re-examination of the balance between CNNs and Transformers in building robust and interpretable models. As Transformer-based architectures continue to evolve, their role in AI and machine learning, particularly in human-centric applications, will likely expand.