基于多尺度特征融合的全分辨率编码器-解码器网络

Jie Ou, Mingjian Chen, Hong Wu
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引用次数: 0

摘要

为了实现更精确的二维人体姿态估计,我们从三方面扩展了成功的编码器-解码器网络,简单基线网络(SBN)。为了减少由于输出步幅过大造成的量化误差,在简单基线网络的末端增加了两个解码器模块,以获得完整的输出分辨率。然后,将全局上下文块(global context block, gcb)添加到编码器和解码器模块中,使其具有全局上下文特性。此外,我们提出了一种新的基于空间注意力的多尺度特征收集和分布模块(SA-MFCD),用于融合和分布多尺度特征,以提高姿态估计的精度。MS COCO数据集上的实验结果表明,我们的网络可以显著提高SBN上人体姿态估计的精度,使用ResNet34作为骨干网的网络甚至可以达到与使用ResNet152的SBN相同的精度,并且我们的网络在大型骨干网上可以取得更好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Full-resolution encoder-decoder networks with multi-scale feature fusion for human pose estimation
To achieve more accurate 2D human pose estimation, we extend the successful encoder-decoder network, simple baseline network (SBN), in three ways. To reduce the quantization errors caused by the large output stride size, two more decoder modules are appended to the end of the simple baseline network to get full output resolution. Then, the global context blocks (GCBs) are added to the encoder and decoder modules to enhance them with global context features. Furthermore, we propose a novel spatial-attention-based multi-scale feature collection and distribution module (SA-MFCD) to fuse and distribute multi-scale features to boost the pose estimation. Experimental results on the MS COCO dataset indicate that our network can remarkably improve the accuracy of human pose estimation over SBN, our network using ResNet34 as the backbone network can even achieve the same accuracy as SBN with ResNet152, and our networks can achieve superior results with big backbone networks.
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