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Advancing Dense Prediction Methods for Visual Scene Understanding

Assistant Professor Lin Guosheng

Nanyang Technological University

Project Description

The task of visual scene understanding is to develop computer algorithms for automatically understanding and analysing the content of scene images and videos, which is a fundamental problem for computer vision and machine learning research. Existing methods for scene understanding are still far from human performance in terms of prediction accuracy, semantic richness and learning efficiency. In this project, we address a number of challenging problems for approaching human-level scene understanding. We will develop novel scene understanding methods based on the recent development of semantic segmentation methods, but go beyond the conventional category-level recognition in existing methods in terms of learning paradigm and semantic concepts for prediction. Specifically, we will focus on instance-level semantic segmentation, high-level semantics recognition including relation recognition and scene description generation, learning from web data with weak annotations, and learning from synthetic data.

Research Technical Area

Computer vision

Benefits to the society

Developed techniques can be applied in video surveillance, robotic systems, multimedia applications, and etc. These application will Improve community safety, transportation, manufacturing and generally human living qualities.

Team's Principal Investigator

Assistant Professor Lin Guosheng
School of Computer Science and Engineering
College of Engineering
Nanyang Technological University

Introduction of the Principal Investigator

PI LIN Guosheng is currently an Assistant professor in the School of Computer Science and Engineer, Nanyang Technological University.  He received his PhD degree from The University of Adelaide, Australia in 2014. His research interests are in machine learning and computer vision applications.

Recent Notable Awards

  • Google PhD fellowship, 2014

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