Pay Attention Selectively and Comprehensively

Deep neural network with multi-scale feature fusion has achieved great success in human pose estimation. However, drawbacks still exist in these methods: 1) they consider multi-scale features equally, which may over-emphasize redundant features; 2) preferring deeper structures, they can learn features with the strong semantic representation, but tend to lose natural discriminative information; 3) to attain good performance, they rely heavily on pretraining, which is time-consuming, or even unavailable practically. To mitigate these problems, we propose a novel comprehensive recalibration model called Pyramid GAting Network (PGA-Net) that is capable of distillating, selecting, and fusing the discriminative and attention-aware features at different scales and different levels (i.e., both semantic and natural levels). Meanwhile, focusing on fusing features both selectively and comprehensively, PGA-Net can demonstrate remarkable stability and encouraging performance even without pre-training, making the model can be trained truly from scratch. We demonstrate the effectiveness of PGA-Net through validating on COCO and MPII benchmarks, attaining new state-of-the-art performance. https://github.com/ssr0512/PGA-Net