XPose: Explainable Human Pose Estimation Framework with Group Shapley Value

Khái niệm cốt lõi

Integrating XAI principles into pose estimation enhances model transparency and interpretability through Group Shapley Value.

Tóm tắt

This paper introduces XPose, a framework that integrates Explainable AI (XAI) principles into pose estimation. It proposes a novel concept called Group Shapley Value (GSV) to assess the contribution of each keypoint to final predictions. The paper also introduces a data augmentation technique known as Group-based Keypoint Removal (GKR) to enhance the model's ability to infer invisible keypoints. Experimental results across three typical methods demonstrate the effectiveness of the proposed approach.
Directory:

Abstract

Proposes XPose framework integrating XAI into pose estimation.

Introduction

Discusses the importance of understanding model decisions.

Methodology

Introduces GSV for assessing keypoint contributions.
Describes GKR data augmentation for enhancing inference.

Experiment

Evaluates XPose on COCO dataset with different occlusion ratios.

Conclusion

Summarizes the contributions and effectiveness of XPose.

Thống kê

"Current approaches in pose estimation primarily concentrate on enhancing model architectures."
"The application of Shapley value to pose estimation has been hindered by prohibitive computational demands."
"Group Shapley Value strategically organizes keypoints into clusters based on their interdependencies."

Trích dẫn

"The emergence of eXplainable AI (XAI) has addressed the opacity and lack of interpretability associated with advanced machine learning models."
"XPose aims to elucidate the individual contribution of each keypoint to final prediction, thereby elevating the model’s transparency and interpretability."

Thông tin chi tiết chính được chắt lọc từ

XPose

by Luyu Qiu,Jia... lúc arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12370.pdf

Yêu cầu sâu hơn

How can XAI principles be applied to other computer vision tasks beyond pose estimation

XAI principles can be applied to other computer vision tasks beyond pose estimation by enhancing the transparency and interpretability of model decisions. For instance, in object detection tasks, XAI techniques can help explain why certain objects are detected or missed in an image. By understanding the rationale behind model predictions, researchers and practitioners can gain insights into how the model processes visual information and make more informed decisions about model improvements.

What are potential drawbacks or limitations of using Group Shapley Value in pose estimation models

One potential drawback of using Group Shapley Value in pose estimation models is the computational complexity involved in clustering keypoints and calculating shapley values within and across groups. As the number of keypoints increases, the computation required for fine-grained shapley value calculations may become prohibitive. Additionally, defining meaningful keypoint clusters based on interdependencies may introduce biases or inaccuracies if not done carefully. Moreover, interpreting group-level shapley values accurately requires a deep understanding of keypoint relationships, which could be challenging to capture comprehensively.

How might advancements in XAI impact the future development of deep learning technologies

Advancements in XAI have the potential to significantly impact the future development of deep learning technologies by promoting trustworthiness, accountability, and fairness in AI systems. With improved explainability through methods like Group Shapley Value introduced in XPose framework for pose estimation models, developers can better understand how models arrive at their decisions. This increased transparency can lead to more robust models with reduced bias and improved performance across various applications such as healthcare diagnostics, autonomous vehicles, natural language processing systems among others.