I am currently a Ph.D. student in the University of Illinois Urbana-Champaign (UIUC), advised by Alexander Schwing. Previously, I obtained a MPhil degree from The Hong Kong University of Science and Technology (HKUST)1, advised by Yu-Wing Tai and Chi-Keung Tang.
I am recently working on scalable (long-term and open-world) video segmentation and understanding. I am generally interested in artificial intelligence. I believe in AGIs and has high hope for their potential to transform human civilization for the better.
I got my first taste of programming in secondary school, making maps (similar to mods) for a game Warcraft III. It started as an anti-cheat patch for an existing map (兵戰), and I later started adding new features. At some point, I completely reprogrammed the entire map. The tradeoff between game balancing and fun was the most daunting task.
I joined the HKUST Robotics Team in UG year 1. We missed champion in Hong Kong Robocon 2016 with the lack of experience and struck back to win champion in 2017. We represented Hong Kong in the International Robocon 2017 competition in Tokyo and took home the Best Design Award.
I developed various low-level libraries (communication protocol, PID, peripheral sensors) and high-level functionalities (path planning, state machines), and picked up cruical engineering/problem solving skills in this journey.
I was the chairperson for the 8th Robot Design Contest in HKUST for new member recruitment. Grateful to this amazing team and all the enthusiastic teammates!
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement
Ho Kei Cheng*, Jihoon Chung*, Yu-Wing Tai, Chi-Keung Tang. Published in CVPR, 2020.
Refines segmentations (4K and beyond) in a class-agnostic manner without using any high-resolution training data through a set of carefully designed cascade operations.
Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion
Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang. Published in CVPR, 2021. Used by: [Sieve], [Trioscope]
A more user-friendly and efficient paradigm of iVOS in which interactions and propagations are decoupled, with the user’s intention captured by a novel difference-aware fusion module.
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation
Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang. Published in NeurIPS, 2021.
Rethinks mask tracking as an image correspondence problem and uses L2 similarity to encourage diversified voting -- simpler, better, and more efficient.
