Learning Feature Pyramids for Human Pose Estimation (1708.01101v1)

Abstract: Articulated human pose estimation is a fundamental yet challenging task in computer vision. The difficulty is particularly pronounced in scale variations of human body parts when camera view changes or severe foreshortening happens. Although pyramid methods are widely used to handle scale changes at inference time, learning feature pyramids in deep convolutional neural networks (DCNNs) is still not well explored. In this work, we design a Pyramid Residual Module (PRMs) to enhance the invariance in scales of DCNNs. Given input features, the PRMs learn convolutional filters on various scales of input features, which are obtained with different subsampling ratios in a multi-branch network. Moreover, we observe that it is inappropriate to adopt existing methods to initialize the weights of multi-branch networks, which achieve superior performance than plain networks in many tasks recently. Therefore, we provide theoretic derivation to extend the current weight initialization scheme to multi-branch network structures. We investigate our method on two standard benchmarks for human pose estimation. Our approach obtains state-of-the-art results on both benchmarks. Code is available at https://github.com/bearpaw/PyraNet.

• The paper introduces a Pyramid Residual Module that significantly improves scale invariance in DCNNs for human pose estimation.
• It employs a multi-branch network architecture with a novel weight initialization scheme tailored for multi-scale feature learning.
• Experimental results on MPII and LSP datasets demonstrate state-of-the-art performance, notably improving detection accuracy for challenging body parts.

Learning Feature Pyramids for Human Pose Estimation

The paper under discussion introduces a methodological advancement in articulated human pose estimation, focusing on addressing challenges related to scale variations of human body parts. This task is crucial in the domain of computer vision and has widespread applications, including activity recognition and human-computer interaction. Despite previous advancements using deep convolutional neural networks (DCNNs), the handling of scale changes via learning feature pyramids within DCNNs has been underexplored.

Key Contributions

The authors propose a Pyramid Residual Module (PRM) designed to improve the scale invariance of DCNNs. The PRM is integrated within a multi-branch network architecture, where convolutional filters are learned across various input feature scales. This approach aims to enhance the robustness of pose detectors against scale variations induced by foreshortening and camera view changes. A theoretical derivation for initializing weights in the multi-branch network is also provided, ensuring that performance gains are not hindered by improper initialization.

Experimental Results

The paper reports state-of-the-art results on two standard benchmarks for human pose estimation: the MPII human pose dataset and the Leeds Sports Poses (LSP) dataset. The proposed method achieves substantial improvements, particularly at detecting challenging body parts such as wrists and ankles, reflecting its efficacy in handling scale variations. On MPII, the method attains a [email protected] score of 92.0%, surpassing prior state-of-the-art results. Similarly, on the LSP dataset, the model demonstrates a marked improvement with a PCK score of 93.9% at a threshold of 0.2.

Theoretical Implications

The paper explores the theoretical underpinnings of weight initialization for multi-branch networks, extending traditional schemes like Xavier and MSR. The proposed initialization scheme incorporates the number of network branches, potentially influencing future work on multi-branch networks which are becoming increasingly relevant in advanced neural architectures.

Practical Implications

Practically, the design of the Pyramid Residual Module (PRM) has broader implications for enhancing the invariance properties of DCNNs in various applications beyond pose estimation. The ability to effectively handle scale variations without excessive computational demands promises advancements in fields where multi-scale analysis is crucial.

Future Perspectives

Looking ahead, the Pyramid Residual Module concept could be adapted across different fields requiring scale-invariant feature learning, such as semantic segmentation and object detection. The simplicity and flexibility of the PRM design could facilitate its integration into other contemporary architectures like ResNets and Inception models, potentially offering performance boosts across a range of tasks.

Conclusion

The proposed methodology in this paper offers a nuanced approach to tackling the issue of scale variation in human pose estimation tasks. Through the introduction of the Pyramid Residual Module and a refined weight initialization scheme for multi-branch networks, the authors have contributed significantly to both the theoretical and practical dimensions of deep learning in computer vision. This work lays the groundwork for further exploration and application of feature pyramidal structures in neural network architectures.