Extracting features containing both high-resolution and high-level-semantic information is important for human pose estimation, while most existing methods of extracting high-resolution high-level-semantic features suffer from spatial information loss or semantic information mismatch. To efficiently address these issues, we propose a novel Dilation Pyramid Module (DPM), which can enlarge the receptive field multiplicatively to achieve high-level-semantic information as subsampling without reducing spatial resolution. DPM is composed of several consecutive dilated convolution layers of which dilation radius is specially designed to enlarge the receptive field multiplicatively and avoid the gridding issue of dilated convolution. Based on DPM, the Dilation Pyramid Net (DPN) is proposed to efficiently extract high-resolution high-level-semantic features. We experimentally demonstrate the effectiveness and efficiency of the proposed DPN
with comparable performance to the state-of-the-art methods over two challenging benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset.

Efficient High-Resolution High-Level-Semantic Representation Learning for Human Pose Estimation

PS T3.10: Computer Vision, automotive and robotics applications

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
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Lisi Guan

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Efficient High-Resolution High-Level-Semantic Representation Learning for Human Pose Estimation

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