Sanghun Jung

I am a second-year PhD student at the University of Washington, where I am advised by Byron Boots. I am interested in visual learning for robotics, bridging the gap between visual perception and robot planning/control. My recent research covers effective transfer learning for LiDAR segmentation and traversability prediction from visual cues by self-supervision.

Mail: shjung13 [at] cs [dot] washington [dot] edu

CV / LinkedIn / Google Scholar / Github

Research

	DARPA Robotic Autonomy in Complex Environments with Resiliency (RACER) University of Washington, 2023
	V-STRONG: Visual Self-Supervised Traversability Learning for Off-road Navigation Sanghun Jung, JoonHo Lee, Xiangyun Meng, Byron Boots, and Alexander Lambert ICRA, 2024 arXiv
	LiDAR-UDA: Self-ensembling Through Time for Unsupervised LiDAR Domain Adaptation Amirreza Shaban, JoonHo Lee, Sanghun Jung, Xiangyun Meng, and Byron Boots ICCV, 2023 Oral Presentation (<1.8%)* arXiv / code
	CAFA: Class-aware Feature Alignment for Test-time Adaptation Sanghun Jung, Jungsoo Lee, Nanhee Kim, Amirreza Shaban, Byron Boots, and Jaegul Choo ICCV, 2023 arXiv
	DebiasBench: Benchmark for Fair Comparison of Debiasing in Image Classification Jungsoo Lee, Juyoung Lee, Sanghun Jung, and Jaegul Choo Preprint, 2023 arXiv
	CG-NeRF: Conditional Generative Neural Radiance Fields Kyungmin Jo, Gyumin Shim, Sanghun Jung, Soyoung Yang, and Jaegul Choo WACV, 2023 arXiv
	3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations with Unposed 2D Images Minsoo Lee, Chaeyeon Chung, Hojun Cho, Minjung Kim, Sanghun Jung, Minhyuk Sung, and Jaegul Choo Preprint, 2022 arXiv
	Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation Sanghun Jung, Jungsoo Lee, Daehoon Gwak, Sungha Choi, and Jaegul Choo ICCV, 2021 Oral Presentation (<3.0%) arXiv / code
	RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening Sungha Choi, Sanghun Jung, Huiwon Yun, Joanne Taery Kim, and Jaegul Choo CVPR, 2021 Oral Presentation (<4.1%) arXiv / code
	Visualizing for the Non-Visual: Enabling the Visually Impaired to Use Visualization Jinho Choi, Sanghun Jung, Deokgun Park, Jaegul Choo, and Niklas Elmqvist EuroVIS, 2019

Work Experience

Applied Scientist Intern - Summer 2024 | Amazon Lab126
Bellevue, WA | Jun. 2024 ~ Sep. 2024

Visual affordance learning
Robot manipulation
Vision-language model / Large-language model

	Robotics Engineer \| BearRobotics Korea, Inc. Seoul, South Korea \| Apr. 2019 ~ Jul. 2020 Safe velocity controller Odometry and localization testing Automated simulation testing infrastructure
	Robotics Engineering Intern \| BearRobotics, Inc. Redwood City, CA, US \| Jul. 2018 ~ Mar. 2019 Depth camera extrinsic calibration

Invited Talk

	2nd Pre-Training for Robot Learning Workshop @ CoRL 2023 Gave a spotlight talk about image-based traversability for off-road navigation
	KAIST AI Conference Presented my recent paper "Standardized Max Logits" during a poster session
	Naver AI LAB Guest speaker for corporate seminar (RobustNet)
	Hyundai Motor Group AI Research Seminar Guest speaker for corporate seminar (RobustNet)

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