InsPose: Instance-Aware Networks for Single-Stage Multi-Person Pose Estimation

• URL: http://arxiv.org/abs/2107.08982v2
• Date: Tue, 20 Jul 2021 01:34:59 GMT
• Title: InsPose: Instance-Aware Networks for Single-Stage Multi-Person Pose Estimation
• Authors: Dahu Shi, Xing Wei, Xiaodong Yu, Wenming Tan, Ye Ren, Shiliang Pu
• Abstract summary: We present a simple yet effective solution by employing instance-aware dynamic networks. Specifically, we propose an instance-aware module to adaptively adjust (part of) the network parameters for each instance. Our solution can significantly increase the capacity and adaptive-ability of the network for recognizing various poses, while maintaining a compact end-to-end trainable pipeline.
• Score: 37.80984212500406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-person pose estimation is an attractive and challenging task. Existing methods are mostly based on two-stage frameworks, which include top-down and bottom-up methods. Two-stage methods either suffer from high computational redundancy for additional person detectors or they need to group keypoints heuristically after predicting all the instance-agnostic keypoints. The single-stage paradigm aims to simplify the multi-person pose estimation pipeline and receives a lot of attention. However, recent single-stage methods have the limitation of low performance due to the difficulty of regressing various full-body poses from a single feature vector. Different from previous solutions that involve complex heuristic designs, we present a simple yet effective solution by employing instance-aware dynamic networks. Specifically, we propose an instance-aware module to adaptively adjust (part of) the network parameters for each instance. Our solution can significantly increase the capacity and adaptive-ability of the network for recognizing various poses, while maintaining a compact end-to-end trainable pipeline. Extensive experiments on the MS-COCO dataset demonstrate that our method achieves significant improvement over existing single-stage methods, and makes a better balance of accuracy and efficiency compared to the state-of-the-art two-stage approaches.

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