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Title: 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 multiscale representations, obtained by the disentangled water-fall module in BAPose, leverage the efficiency of progressive filtering in the cascade architecture, while maintaining multi-scale fields-of- view comparable to spatial pyra-mid configurations. Our results on the challenging COCO and CrowdPose datasets demonstrate that BAPose is an efficient and robust framework for multi-person pose estimation, significantly improving state-of-the-art accuracy.  more » « less
Award ID(s):
1749376
NSF-PAR ID:
10399718
Author(s) / Creator(s):
;
Date Published:
Journal Name:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
Page Range / eLocation ID:
528 - 537
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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