Hi, I'm Chanuka Wijayakoon

I am a Research Engineer at the School of Information Systems, in Singapore Management University. Currently engaged in computer vision and machine learning research. I'm an avid reader and a photography enthusiast.

I am looking for opportunities in Computer Vision and Deep Learning research domains.

Last updated: July 2019

Email chanuka [dot] 14 [at] cse.mrt.ac.lk
Mobile +65 86729216
Blog blog.chanukawijayakoon.me
Research Work

My current research focuses on using Genetic Algorithms to generate visualizations of images at the decision boundary of CNN classifiers. This project is carried out under the guidance of Prof. Jun Sun at the Singapore Management University.

Research Projects
Video Colorization Dataset & Benchmark

Compared to its peer, image colorization, video colorization is a relatively unexplored area in computer vision. Most of the models available for video colorization are extensions of image colorization, and hence are unable to address some unique issues in video domain.

In this project, we evaluated the applicability of image colorization techniques for video colorization, identifying problems inherent to videos and attributes affecting them. We developed a dataset and benchmark to measure the effect of such attributes to video colorization quality and demonstrate how our benchmark aligns with human evaluations.

FlowChroma - A Deep Recurrent Network for Video Colorization

We developed an automated video colorization framework that minimizes the flickering of colors across frames. If we apply image colorization techniques to successive frames of a video, they treat each frame as a separate colorization task. Thus, they do not necessarily maintain the colors of a scene consistently across subsequent frames.

The proposed solution includes a novel deep recurrent encoder-decoder architecture which is capable of maintaining temporal and contextual coherence between consecutive frames of a video. We use a high-level semantic feature extractor to automatically identify the context of a scenario, with a custom fusion layer that combines the spatial and temporal features of a frame sequence.