Yongxin (Richard) Wang

I am an Applied Scientist at Amazon AWS AI, under the Rekognition team providing image analysis service to customers. I primaliry work on topics related to face recognition.

Prior to Amazon, I obtained my Master of Science in Computer Vision (MSCV) Degree at the Robotics Insitute of Carnegie Mellon University. I work with Prof. Kris Kitani on Multi-Object Tracking (MOT), and Prof. Louis-Philippe Morency on Multimodal Machine Learning (MMML). I obtained my Bachelor's Degrees from Georgia Institute of Technology with double majors in Computer Science and Industrial Engineering. I also have worked with Prof. Jim Rehg on deep learning based human gaze analysis.

yongxinw [at] amazon.com / CV / Google Scholar / LinkedIn

Research

I'm interested in Computer Vision, Multimodal Machine Learning, and Robotics. My research footprints have covered Multi-Object Tracking (MOT), Multimodal Human Language Sequences, and human gaze tracking. I want to one day build a robot/agent that understands the intriguing human behaviors and communicates naturally with humans. I've also got some experience in data visualization.

	MTAG: Modal-Temporal Attention Graph for Unaligned Human Multimodal Language Sequences Jianing Yang, Yongxin Wang, Ruitao Yi, Yuying Zhu, Azaan Rehman, Amir Zadeh, Soujanya Poria, Louis-Philippe Morency Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), 2021 code / bibtex Modal-Temporal Graph for analysing unaligned human language sequences. (* indicates equal contribution)
	Joint Object Detection and Multi-Object Tracking with Graph Neural Networks Yongxin Wang, Kris M. Kitani, Xinshuo Weng International Conference on Robotics and Automation (ICRA) 2021 code / website / slides / bibtex Joint detection and association using Graph Neural Networks. Named GSDT on MOTChallenge.
	GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with 2D-3D Multi-Feature Learning Xinshuo Weng, Yongxin Wang, Kris M. Kitani Computer Vision and Pattern Recognition (CVPR), 2020 code / website / slides / bibtex State-of-the-art performance in 3D MOT in KITTI dataset
	Detecting Attended Visual Targets in Video Eunji Chong, Yongxin Wang, Nataniel Ruiz , James M. Rehg Computer Vision and Pattern Recognition (CVPR), 2020 code / dataset / bibtex Predicting where the people are looking at in videos.
	Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency Eunji Chong, Nataniel Ruiz , Yongxin Wang, Yun Zhang, Agata Rozga, James M. Rehg European Conference on Computer Vision (ECCV), 2018 poster / bibtex Predicting where the people are looking at.
	TypoTweet Maps: Characterizing Urban Areas through Typographic Social Media Visualization Alex Godwin, Yongxin Wang, John T. Stasko, European Conference on Visualization (EuroVis), 2017 bibtex Visualizing social media data in a Typographic map.

Experiences

	Amazon Rekognition, Mar. 2020 - Present Applied Scientist Working on face recognition services and technologies.
	Carnegie Mellon University, Jan. 2019 - Present Research Assistant with Prof. Kris Kitani Worked on simultaneous detection and associate with Graph Neural Networks for Multi-Object Tracking
	Carnegie Mellon University, Aug. 2019 - Present Research Assistant with Prof. Louis-Philippe Morency Worked on modeling multimodal temporal languange sequences with Graph Neural Networks
	Amazon Rekognition, May. 2019 - Aug. 2019 Applied Scientist Intern with Dr. Wei Xia Worked on high-resolution face synthesis with disentangled control through facial identity and attributes
	Georgia Institute of Technology, Jan. 2017 - May. 2018 Research Assistant Intern with Prof. Jim Rehg Worked on gaze target prediction in image and in video

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