Perception and Intelligence Laboratory


Recent Publications

MoST: Motion Style Transformer between Diverse Action Contents (CVPR, 2024)

Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding 

(AAAI, 2024)

Quantitative Manipulation of Custom Attributes on 3D-Aware Image Synthesis (CVPR, 2023)

Balanced Energy Regularization Loss for Out-of-distribution Detection (CVPR, 2023)

Confidence-Based Feature Imputation for Graphs with Partially Known Features (ICLR, 2023)

Tracking Failure Prediction for Siamese Trackers Based on Channel Feature Statistics (AVSS, 2022)


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.