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Coupled Recurrent Network (CRN)
Lin Sun, Kui Jia, Yuejia Shen +3
Many semantic video analysis tasks can benefit from multiple, heterogenous signals. For example, in addition to the original RGB input sequences, sequences of optical flow are usua…
Adversarial 3D Human Pose Estimation via Multimodal Depth Supervision
Kun Zhou, Jinmiao Cai, Yao Li +5
In this paper, a novel deep-learning based framework is proposed to infer 3D human poses from a single image. Specifically, a two-phase approach is developed. We firstly utilize a…
Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data
Yabin Zhang, Hui Tang, Kui Jia
Fine-grained visual categorization (FGVC) is challenging due in part to the fact that it is often difficult to acquire an enough number of training samples. To employ large models…
FBI-Pose: Towards Bridging the Gap between 2D Images and 3D Human Poses using Forward-or-Backward Information
Yulong Shi, Xiaoguang Han, Nianjuan Jiang +3
Although significant advances have been made in the area of human poses estimation from images using deep Convolutional Neural Network (ConvNet), it remains a big challenge to perf…
Part-Aware Fine-grained Object Categorization using Weakly Supervised Part Detection Network
Yabin Zhang, Kui Jia, Zhixin Wang
Fine-grained object categorization aims for distinguishing objects of subordinate categories that belong to the same entry-level object category. The task is challenging due to the…