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Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders (CVPR, 2021)  
Motion-aware Ensemble of Three-mode Trackers for Unmanned Aerial Vehicles (MVA 2021)  
AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep Networks (AAAI, 2021)  
Class-Attentive Diffusion Network for Semi-Supervised Classification (AAAI, 2021)  
Separating Particulate Matter From a Single Microscopic Image (CVPR, 2020)  

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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