About the Research in
ITU Vision Laboratory
The development of computer vision research is faster than ever before. It has never attracted so society’s attention. We believe AI-based technology will be the main assistant of humanity in the future. Developing and deploying cutting-edge computer vision science to shape the changing world is one of the most important strategic decision for societies. Contributing to the development in progress is the main purpose of Vision Lab. Therefore, We —grad students, undergrads, postdocs— deploy cutting-edge scientific research and produce papers on Computer Vision.
Continual Learning is a concept to learn a model for a large number of tasks sequentially without forgetting knowledge obtained from the preceding tasks, where the data in the old tasks are not available any more during training new ones.
Extraction of 3D information from digital images. By comparing information from two vantage points, 3D information can be extracted from relative positions of objects in the two panels.
Uncertainty and Explainable AI
Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by humans.
In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data.
Our Research Topics
- Predictive Modeling
- Explainable AI
- Representation Learning
- Depth Estimation
- Unsupervised Domain Adaptation
- Anomaly Detection
- Point Clouds
- Medical Image Analysis