Meta Agent Teaming Active Learning for Pose Estimation

Gong, Jia and Fan, Zhipeng and Ke, Qiuhong and Rahmani, Hossein and Liu, Jun (2022) Meta Agent Teaming Active Learning for Pose Estimation. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, pp. 11069-11079. ISBN 9781665469470

[img]
Text (Gong_Meta_Agent_Teaming_Active_Learning_for_Pose_Estimation_CVPR_2022_paper)
Gong_Meta_Agent_Teaming_Active_Learning_for_Pose_Estimation_CVPR_2022_paper.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (1MB)
[img]
Text (GJ_CVPR_2022_Agent_Teaming)
GJ_CVPR_2022_Agent_Teaming.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (1MB)

Abstract

The existing pose estimation approaches often require a large number of annotated images to attain good estimation performance, which are laborious to acquire. To reduce the human efforts on pose annotations, we propose a novel Meta Agent Teaming Active Learning (MATAL) framework to actively select and label informative images for effective learning. Our MATAL formulates the image selection procedure as a Markov Decision Process and learns an optimal sampling policy that directly maximizes the performance of the pose estimator based on the reward. Our framework consists of a novel state-action representation as well as a multi-agent team to enable batch sampling in the active learning procedure. The framework could be effectively optimized via Meta-Optimization to accelerate the adaptation to the gradually expanded labeled data during deployment. Finally, we show experimental results on both human hand and body pose estimation benchmark datasets and demonstrate that our method significantly outperforms all baselines continuously under the same amount of annotation budget. Moreover, to obtain similar pose estimation accuracy, our MATAL framework can save around 40% labeling efforts on average compared to state-of-the-art active learning frameworks.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
179341
Deposited By:
Deposited On:
19 Jan 2023 16:20
Refereed?:
Yes
Published?:
Published
Last Modified:
02 Feb 2023 02:01