Human Pose Estimation with Iterative Error Feedback
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Hierarchical feature extractors such as Convolutional Networks (ConvNets) have achieved impressive performance on a variety of classification tasks using purely feedforward processing. Feedforward architectures can learn rich representations of the input space but do not explicitly model dependencies in the output spaces, that are quite structured for tasks such as articulated human pose estimation or object segmentation. Here we propose a framework that expands the expressive power of hierarchical feature extractors to encompass both input and output spaces, by introducing top-down feedback. Instead of directly predicting the outputs in one go, we use a self-correcting model that progressively changes an initial solution by feeding back error predictions, in a process we call Iterative Error Feedback (IEF). IEF shows excellent performance on the task of articulated pose estimation in the challenging MPII and LSP benchmarks, matching the state-of-the-art without requiring ground truth scale annotation.
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Iterative refinement using the whole network, termed recycling, contributes markedly to accuracy with minor extra training time.
“The iterative refinement using the whole network (which we term ‘recycling’ and is related to approaches in computer vision28,29) contributes markedly to accuracy with minor extra training time (see Supplementary Methods 1.8 for details).”
From Highly accurate protein structure prediction with AlphaFold (2021) · cites this paper as [29]
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- Authors
- Carreira, J., Agrawal, P., Fragkiadaki, K., & Malik, J.
- Affiliations
- Google (United States) (US)
- University of California, Berkeley (US)
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 4733-4742
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1537698211
- Cited by
- 805
- References
- 53
- Citations by year
- 2026: 20 · 2025: 41 · 2024: 68 · 2023: 77 · 2022: 88 · 2021: 111 · 2020: 100 · 2019: 101 · 2018: 75 · 2017: 68 · 2016: 53 · 2015: 2 · 2014: 1
- FWCI
- 34.34
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nvidia
- Multidisciplinary University Research Initiative (N00014-10-1-0933)
- Fundação para a Ciência e a Tecnologia (SFRH/BPD/84194/2012)
- Office of Naval Research (N00014-10-1-0933, N000141410671, N00014)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- human pose estimation, articulated pose estimation, top-down feedback, hierarchical feature extraction, error prediction
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