Convolutional Pose Machines
Abstrak
Pose Machines provide a sequential prediction framework for learning rich implicit spatial models. In this work we show a systematic design for how convolutional networks can be incorporated into the pose machine framework for learning image features and image-dependent spatial models for the task of pose estimation. The contribution of this paper is to implicitly model long-range dependencies between variables in structured prediction tasks such as articulated pose estimation. We achieve this by designing a sequential architecture composed of convolutional networks that directly operate on belief maps from previous stages, producing increasingly refined estimates for part locations, without the need for explicit graphical model-style inference. Our approach addresses the characteristic difficulty of vanishing gradients during training by providing a natural learning objective function that enforces intermediate supervision, thereby replenishing back-propagated gradients and conditioning the learning procedure. We demonstrate state-of-the-art performance and outperform competing methods on standard benchmarks including the MPII, LSP, and FLIC datasets.
Topik & Kata Kunci
Penulis (4)
S. Wei
V. Ramakrishna
T. Kanade
Yaser Sheikh
Akses Cepat
- Tahun Terbit
- 2016
- Bahasa
- en
- Total Sitasi
- 2894×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1109/CVPR.2016.511
- Akses
- Open Access ✓