Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation (1705.02407v2)

Abstract: Human pose estimation using deep neural networks aims to map input images with large variations into multiple body keypoints which must satisfy a set of geometric constraints and inter-dependency imposed by the human body model. This is a very challenging nonlinear manifold learning process in a very high dimensional feature space. We believe that the deep neural network, which is inherently an algebraic computation system, is not the most effecient way to capture highly sophisticated human knowledge, for example those highly coupled geometric characteristics and interdependence between keypoints in human poses. In this work, we propose to explore how external knowledge can be effectively represented and injected into the deep neural networks to guide its training process using learned projections that impose proper prior. Specifically, we use the stacked hourglass design and inception-resnet module to construct a fractal network to regress human pose images into heatmaps with no explicit graphical modeling. We encode external knowledge with visual features which are able to characterize the constraints of human body models and evaluate the fitness of intermediate network output. We then inject these external features into the neural network using a projection matrix learned using an auxiliary cost function. The effectiveness of the proposed inception-resnet module and the benefit in guided learning with knowledge projection is evaluated on two widely used benchmarks. Our approach achieves state-of-the-art performance on both datasets.

• The paper introduces Knowledge-Guided Deep Fractal Neural Networks, a novel approach to human pose estimation that incorporates external human knowledge into the network architecture.
• The proposed method achieves state-of-the-art performance on benchmark datasets like MPII and LSP, demonstrating improved accuracy (PCK, PCP) compared to existing techniques.
• Key contributions include a framework for representing and projecting external knowledge to guide deep neural network training and a fractal network design using inception-resnet modules to capture multi-scale dependencies.

Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation

The paper "Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation" introduces a novel approach to the task of human pose estimation, an essential aspect of computer vision with applications in action recognition, human-computer interaction, and motion capture. The authors propose innovative methodology by incorporating external human knowledge into the architecture of deep neural networks to improve the accuracy and robustness of human pose estimation from single input images.

Technical Approach

The fundamental challenge in human pose estimation arises from the need to map input images of considerable variance into multiple body keypoints constrained by geometric dependencies inherent in human body models. Traditionally, solutions such as Pictorial Structure (PS) models have been utilized to address these spatial relations, although they necessitate manual design and are limited in their ability to capture nonlinear dependencies effectively.

To overcome these limitations, this paper proposes a deep fractal network construction utilizing the stacked hourglass design enhanced with inception-resnet modules. This fractal network approach operates without explicit graphical modeling, instead regressing images into structured heatmaps which offer a representation of body keypoints. This process is aided by encoding external knowledge in the form of visual features that characterize human body models. During training, these features are injected into the neural networks via a learned projection matrix, which assists in reinforcing and guiding the learning process.

Experimental Results

Testing was conducted on two datasets extensively used in human pose estimation research: the MPII Human Pose dataset and the Leeds Sports Pose dataset. The paper reports that the proposed methodology achieves state-of-the-art performance metrics on these datasets. Specifically, their approach demonstrates higher accuracies on evaluation metrics such as PCK (Percentage of Correct Keypoints) and PCP (Percentage of Correct Parts) compared to existing methods. Notably, they observe performance improvements attributable to their strategy of injecting knowledge and employing inception-resnet modules, highlighting the enhanced feature representation and learning guidance.

Contributions and Implications

The paper makes several key contributions to the field of human pose estimation:

1. Knowledge Representation: It provides a framework to represent and project external human knowledge guiding the DNN training process. This approach is generic and potentially extendable to other deep learning applications.
2. Fractal Network Design: The introduction of a fractal network design employing inception-resnet modules captures multi-scale interdependencies between body joints. This design effectively enhances the network's capability in modeling complex data without increasing computational complexity during inference.

Future Directions

The concept of knowledge-guided learning opens up intriguing possibilities for future research in AI. Future studies could explore extending this framework to other domains and network architectures, potentially leading to more efficient training processes across diverse applications. Additionally, as understanding and incorporating human knowledge become more sophisticated, exploring other forms of external knowledge representation and their impact on model interpretability and robustness could be valuable.

In conclusion, this paper offers a significant contribution to human pose estimation by extending deep learning methodologies with guided learning and innovative network structures, improving accuracy and robustness in handling complex pose variations and constraints. The implications of this work support continued exploration into integrating human knowledge into AI systems, offering pathways to next-generation AI advancements.