  We propose an augmented Parallel-Pyramid Net ($P^2~Net$) with feature
refinement by dilated bottleneck and attention module. During data
preprocessing, we proposed a differentiable auto data augmentation ($DA^2$)
method. We formulate the problem of searching data augmentaion policy in a
differentiable form, so that the optimal policy setting can be easily updated
by back propagation during training. $DA^2$ improves the training efficiency. A
parallel-pyramid structure is followed to compensate the information loss
introduced by the network. We innovate two fusion structures, i.e. Parallel
Fusion and Progressive Fusion, to process pyramid features from backbone
network. Both fusion structures leverage the advantages of spatial information
affluence at high resolution and semantic comprehension at low resolution
effectively. We propose a refinement stage for the pyramid features to further
boost the accuracy of our network. By introducing dilated bottleneck and
attention module, we increase the receptive field for the features with limited
complexity and tune the importance to different feature channels. To further
refine the feature maps after completion of feature extraction stage, an
Attention Module ($AM$) is defined to extract weighted features from different
scale feature maps generated by the parallel-pyramid structure. Compared with
the traditional up-sampling refining, $AM$ can better capture the relationship
between channels. Experiments corroborate the effectiveness of our proposed
method. Notably, our method achieves the best performance on the challenging
MSCOCO and MPII datasets.
