The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Articulated pose estimation with flexible mixtures-of-parts
Abstract
We describe a method for human pose estimation in static images based on a novel representation of part models. Notably, we do not use articulated limb parts, but rather capture orientation with a mixture of templates for each part. We describe a general, flexible mixture model for capturing contextual co-occurrence relations between parts, augmenting standard spring models that encode spatial relations. We show that such relations can capture notions of local rigidity. When co-occurrence and spatial relations are tree-structured, our model can be efficiently optimized with dynamic programming. We present experimental results on standard benchmarks for pose estimation that indicate our approach is the state-of-the-art system for pose estimation, outperforming past work by 50% while being orders of magnitude faster.
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Cites
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Is object localization for free? - Weakly-supervised learning with convolutional neural networks
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Learning Privacy-preserving Optics for Human Pose Estimation
Hinojosa, C., Niebles, J. C., & Arguello, H. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00257 · Cited by 62
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Metadata
- Authors
- Yang, Y., & Ramanan, D.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 1385-1392
- OpenAlex
- W1994529670
- Cited by
- 990
- References
- 4
- FWCI
- 75.33
- Type
- conference paper
- Language
- en
- Open access
- closed
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