BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall Representations
We propose BAPose, a novel bottom-up approach that achieves state-of-the-art results for multi-person pose estimation. Our end-to-end trainable framework leverages a disentangled multi-scale waterfall architecture and incorporates adaptive convolutions to infer keypoints more precisely in crowded scenes with occlusions. The multi-scale representations, obtained by the disentangled waterfall module in BAPose, leverage the efficiency of progressive filtering in the cascade architecture, while maintaining multi-scale fields-of-view comparable to spatial pyramid configurations. Our results on the challenging COCO and CrowdPose datasets demonstrate that BAPose is an efficient and robust framework for multi-person pose estimation, achieving significant improvements on state-of-the-art accuracy.
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Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
---|---|---|---|---|---|---|
Multi-Person Pose Estimation | CrowdPose | BAPose (W32) | mAP @0.5:0.95 | 72.2 | # 6 | |
AP Easy | 79.9 | # 6 | ||||
AP Medium | 73.4 | # 6 | ||||
AP Hard | 61.3 | # 8 | ||||
Multi-Person Pose Estimation | MS COCO | BAPose | AP | 0.727 | # 5 | |
Validation AP | 72.7 | # 3 | ||||
Test AP | 71.2 | # 4 |