I am a PhD student in Computer Science at UCLA, developing computer vision systems in the Center for Vision, Cognition, Learning, and Autonomy advised by Professor Song-Chun Zhu.
My research interests include Computer Vision, Artificial Intelligence, and Machine Learning.
University of California, Los Angeles hangqi (at) cs.ucla.edu
- Paleo won a runner-up price for best real-world application at Southern California Machine Learning Symposium 2017.
Paleo: A Performance Model for Deep Neural Networks
Joint work with Evan R. Sparks and Ameet Talwalkar
Paleo is an analytical model to estimate the scalability and performance of deep learning systems. It can be used for efficiently exploring the space of scalable deep learning systems and quickly diagnosing their effectiveness for a given problem instances. Paleo is robust to the choice of network architecture, hardware, software, communication schemes, and parallelization strategies.Paper » Live Demo » View on Github »
Restricted Visual Turing Test
Joint work with Tianfu Wu, Mun-Wai Lee, and Song-Chun Zhu
This project features a restricted visual Turing test (VTT) which evaluates computer vision systems' understanding of scenes and events in videos by story-line based queries. We collected a long-term and multi-camera captured video dataset. To perform the test, we built an integrated system consisting of a well-designed architecture, various vision modules, a knowledge base, and a query engine.Project Page »
Topic Discovery and Story Segmentation for Broadcast News
Joint work with Weixin Li, Jungseock Joo, and Song-Chun Zhu
Topic discovery and story segmentation provides fundamental methods for automatically organizing, analyzing, searching, and visualizing the vast amount of news videos available online. In this project, we present a topic discovery and story segmentation framework based on Swendsen-Wang Cuts, aiming at dividing news videos into stories and generating a topic hierarchy to organize these stories.Project Page »