Efficient object localization using Convolutional Networks
Abstract
Recent state-of-the-art performance on human-body pose estimation has been achieved with Deep Convolutional Networks (ConvNets). Traditional ConvNet architectures include pooling and sub-sampling layers which reduce computational requirements, introduce invariance and prevent over-training. These benefits of pooling come at the cost of reduced localization accuracy. We introduce a novel architecture which includes an efficient ‘position refinement’ model that is trained to estimate the joint offset location within a small region of the image. This refinement model is jointly trained in cascade with a state-of-the-art ConvNet model [21] to achieve improved accuracy in human joint location estimation. We show that the variance of our detector approaches the variance of human annotations on the FLIC [20] dataset and outperforms all existing approaches on the MPII-human-pose dataset [1].
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Dropout on full convolutional filters improves performance.
“Using dropout on full convolutional filters [31], as opposed to individual weights, improves performance and produces smoother looking weights.”
From Temporal Convolutional Networks for Action Segmentation and Detection (2017) · cites this paper as [31]
Cites
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BackgroundRecent advances in convolutional neural networks
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BackgroundLearning the signatures of the human grasp using a scalable tactile glove
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- Authors
- Tompson, J., Goroshin, R., Jain, A., LeCun, Y., & Bregler, C.
- Affiliations
- New York University (US)
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 648-656
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1936750108
- Cited by
- 1,365
- References
- 1
- Citations by year
- 2026: 34 · 2025: 76 · 2024: 127 · 2023: 157 · 2022: 183 · 2021: 227 · 2020: 187 · 2019: 168 · 2018: 90 · 2017: 71 · 2016: 39 · 2015: 5 · 2014: 1
- FWCI
- 28.03
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Funders
- Office of Naval Research
- 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 › Human-Computer Interaction › Hand Gesture Recognition Systems
- Keywords
- human pose estimation, joint localization, convolutional neural networks, pooling layers, cascade training, object localization
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