  We present an efficient high-resolution network, Lite-HRNet, for human pose
estimation. We start by simply applying the efficient shuffle block in
ShuffleNet to HRNet (high-resolution network), yielding stronger performance
over popular lightweight networks, such as MobileNet, ShuffleNet, and Small
HRNet.
  We find that the heavily-used pointwise (1x1) convolutions in shuffle blocks
become the computational bottleneck. We introduce a lightweight unit,
conditional channel weighting, to replace costly pointwise (1x1) convolutions
in shuffle blocks. The complexity of channel weighting is linear w.r.t the
number of channels and lower than the quadratic time complexity for pointwise
convolutions. Our solution learns the weights from all the channels and over
multiple resolutions that are readily available in the parallel branches in
HRNet. It uses the weights as the bridge to exchange information across
channels and resolutions, compensating the role played by the pointwise (1x1)
convolution. Lite-HRNet demonstrates superior results on human pose estimation
over popular lightweight networks. Moreover, Lite-HRNet can be easily applied
to semantic segmentation task in the same lightweight manner. The code and
models have been publicly available at https://github.com/HRNet/Lite-HRNet.

Computer Vision and Pattern Recognition