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PMnet: Learning of Disentangled Pose and Movement for Unsupervised Motion Retargeting (BMVC, 2019)  
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning (ICCV, 2019)  
A Comprehensive Overhaul of Feature Distillation, (ICCV, 2019)  
Variational Inference for 3-D Localization and Tracking of Multiple Targets Using Multiple Cameras. (TNNLS, 2019)  
Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification. (AAAI, 2019)  

The goal of the PILab is to gain useful technologies on perception and intelligence which can be applied to visual surveillance systems, inspection machines, robots, etc.

We are developing perceptual primitives to detect, track, and recognize human/vehicles, faces and to understand abnormal behaviors and situations through visual information. In addition, we are developing incremental learning models including probabilistic learning machines for integrating multiple sensory modalities in the changing environments.

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