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20162019
most citedHuman Motion Prediction via Learning Local Structure Representations and Temporal Dependencies

13 citations · 23 across the 6 of their papers we have counts for

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7 papers · 1 filter

cs.CV201913 cited

Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies

Xiao Guo, Jongmoo Choi

Human motion prediction from motion capture data is a classical problem in the computer vision, and conventional methods take the holistic human body as input. These methods ignore…

cs.CV2018

Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks

Ye Wang, Yueru Chen, Jongmoo Choi +1

This paper reports a visible and thermal drone monitoring system that integrates deep-learning-based detection and tracking modules. The biggest challenge in adopting deep learning…

cs.CV20181 cited

Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation

Ye Wang, Jongmoo Choi, Yueru Chen +5

Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects. It becomes tremendously challenging when…

cs.CV20181 cited

Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation

Ye Wang, Jongmoo Choi, Yueru Chen +4

One major technique debt in video object segmentation is to label the object masks for training instances. As a result, we propose to prepare inexpensive, yet high quality pseudo g…

cs.CV20171 cited

A Deep Learning Approach to Drone Monitoring

Yueru Chen, Pranav Aggarwal, Jongmoo Choi +1

A drone monitoring system that integrates deep-learning-based detection and tracking modules is proposed in this work. The biggest challenge in adopting deep learning methods for d…

cs.CV20177 cited

Deep 3D Face Identification

Donghyun Kim, Matthias Hernandez, Jongmoo Choi +1

We propose a novel 3D face recognition algorithm using a deep convolutional neural network (DCNN) and a 3D augmentation technique. The performance of 2D face recognition algorithms…