ADORE: An Adaptive Holons Representation Framework for Human Pose Estimation
In this paper, the problem of human pose estimation in a 2D still image is addressed. A framework called adaptive holons representation (ADORE) that takes advantage of local and global cues is proposed to improve the pose estimation accuracy. In particular, ADORE is made up of two components: 1) the holons part, independent losses pose nets (ILPNs) is designed to first infer joints location on the global level; and 2) the adaptive part, convolutional local detectors (CLDs) is proposed to subsequently detect the joints in the potential regions generated by ILPN. Pose estimation is formulated as a classification problem toward body joints in ILPN, which consists of two independent loss layers that, respectively, instruct the learning of <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> coordinates of a joint. Experimental results on two challenging benchmark tasks demonstrate that our proposed framework is more efficient than other deep models and has desirable performance.