GateAttentionPose: Enhancing Pose Estimation with Agent Attention and Improved Gated Convolutions

TL;DR

GateAttentionPose introduces novel attention and gating modules to enhance pose estimation accuracy and efficiency, outperforming existing methods on standard benchmarks and suitable for diverse real-world applications.

Contribution

The paper proposes the Agent Attention module and Gate-Enhanced Feedforward Block, improving computational efficiency and feature extraction in pose estimation architectures.

Findings

Outperforms state-of-the-art on COCO and MPII datasets

Achieves higher accuracy with improved efficiency

Effective in complex scene scenarios

Abstract

This paper introduces GateAttentionPose, an innovative approach that enhances the UniRepLKNet architecture for pose estimation tasks. We present two key contributions: the Agent Attention module and the Gate-Enhanced Feedforward Block (GEFB). The Agent Attention module replaces large kernel convolutions, significantly improving computational efficiency while preserving global context modeling. The GEFB augments feature extraction and processing capabilities, particularly in complex scenes. Extensive evaluations on COCO and MPII datasets demonstrate that GateAttentionPose outperforms existing state-of-the-art methods, including the original UniRepLKNet, achieving superior or comparable results with improved efficiency. Our approach offers a robust solution for pose estimation across diverse applications, including autonomous driving, human motion capture, and virtual reality.