Yucheng Chen

TL;DR: This survey extensively reviews the recent deep learning-based 2D and 3D human pose estimation methods published since 2014 and summarizes the challenges, main frameworks, benchmark datasets, evaluation metrics, performance comparison, and discusses some promising future research directions.

TL;DR: In this paper, a dataset independent translation-scale invariant image mapping method is proposed, which transforms the skeleton videos to colour images, named skeleton-images, and a multi-scale deep convolutional neural network (CNN) architecture is proposed which could be built and fine-tuned on the powerful pre-trained CNNs, e.g., AlexNet, VGGNet, ResNet etal.

TL;DR: This paper proposes a novel recursive coarse-to-fine semantic segmentation framework based on only image-level category labels that can be easily extended to foreground object segmentation task and achieves comparable performance with the state-of-the-art supervised methods on the Internet object dataset.

TL;DR: A video domain translation-scale invariant image mapping is proposed, which transforms the 3D skeleton videos to color images, namely skeleton images, and a multi-scale dilated convolutional neural network is designed for the classification of the skeleton images.

TL;DR: In this article, a coarse-to-fine semantic segmentation framework based on only image-level category labels is proposed, where an initial coarse mask is first generated by a convolutional neural network-based unsupervised foreground segmentation model and then is enhanced by a graph model.