The paper introduces GateAttentionPose, an approach that enhances the UniRepLKNet architecture for pose estimation tasks. The key contributions are:
The authors extensively evaluate GateAttentionPose on the COCO and MPII datasets, demonstrating that it outperforms existing state-of-the-art methods, including the original UniRepLKNet, while achieving superior or comparable results with improved efficiency. The approach offers a robust solution for pose estimation across diverse applications, including autonomous driving, human motion capture, and virtual reality.
The paper first introduces the overall architecture of GateAttentionPose, which includes the GLACE module, the advanced feature extraction backbone, and the multi-scale feature integration and upsampling components. The GLACE module is optimized to transform input images into feature maps, while the backbone integrates the Agent Attention module and the GEFB to enhance feature extraction and computational efficiency.
The authors then present the results of their experiments on the COCO and MPII benchmarks, showing that GateAttentionPose achieves state-of-the-art performance in terms of Average Precision (AP) on the COCO dataset and Percentage of Correct Keypoints with head-normalized distance (PCKh) on the MPII dataset. The model's compact size and efficient design make it suitable for real-world applications with computational constraints.
Finally, the paper concludes by highlighting the key contributions of GateAttentionPose and its potential to advance the field of pose estimation, inspiring further optimizations in visual understanding tasks.
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arxiv.org
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by Liang Feng, ... ב- arxiv.org 09-13-2024
https://arxiv.org/pdf/2409.07798.pdfשאלות מעמיקות