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Context-aware Deep Feature Compression for High-speed Visual Tracking (CVPR, 2018)  
PaletteNet: Image Recolorization with Given Color Palette (CVPR workshop, 2017)  
Anti-Glare: Tightly Constrained Optimization for Eyeglass Reflection Removal​ (CVPR, 2017)  
Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold (CVPR, 2017)  
Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning (CVPR, 2017)  

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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