Sungha Choi
Senior Staff AI Researcher
Qualcomm AI Research
Email / CV / LinkedIn / Google Scholar / GitHub
He is a senior staff engineer at Qualcomm AI Research. Before joining Qualcomm, he was an applied scientist at LG AI Research. He also has over ten years of R&D experience in the automotive infotainment domain at LG Electronics. He received his Ph.D. in the Dept. of Computer Science and Engineering at Korea University in 2022, advised by Prof. Jaegul Choo. He obtained his M.S. in the Dept. of Computer Science and Engineering at Sogang University in 2007 under the supervision of Prof. Jihoon Yang and his B.S. also from Sogang University.
News
7/2024: One paper accepted to ECCV 2024 New!
7/2023: One paper accepted to ICCV 2023
2/2023: Two papers accepted to CVPR 2023
1/2023: One paper accepted to ICLR 2023
7/2022: One paper accepted to ECCV 2022
9/2021: I joined Qualcomm AI Research.
7/2021: One paper accepted to ICCV 2021 (Oral)
2/2021: One paper accepted to CVPR 2021 (Oral)
2/2020: One paper accepted to CVPR 2020
2/2019: One paper accepted to CVPR 2019 (Oral)
Professional Experience
Qualcomm AI Research, Seoul, S. Korea.
Senior Staff AI Researcher, Sep. 2021 - PresentLG AI Research, Seoul, S. Korea.
Applied Scientist in Vision Lab., Dec. 2020 - Aug. 2021Automotive & B2B Center, CTO Division, LG Electronics, Seoul, S. Korea
Lead Software Engineer in Smart Mobility Lab, Jan. 2007 - Nov. 2020
Publications
(† for corresponding author or project lead)
(† for corresponding author or project lead)
(C11) Feature Diversification and Adaptation for Federated Domain Generalization
Seunghan Yang, Seokeon Choi, Hyunsin Park, Sungha Choi, Simyung Chang, and Sungrack Yun
European Conference on Computer Vision (ECCV), 2024, Accepted (27.9% acceptance rate).
[PDF](C10) Towards Open-Set Test-Time Adaptation Utilizing the Wisdom of Crowds in Entropy Minimization
Jungsoo Lee, Debasmit Das, Jaegul Choo, and Sungha Choi†
International Conference on Computer Vision (ICCV), 2023, Accepted (26.2% acceptance rate).
[PDF](C9) EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization
Junha Song, Jungsoo Lee, In So Kweon, and Sungha Choi†
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023, Accepted (25.8% acceptance rate).
[PDF](C8) Progressive Random Convolutions for Single Domain Generalization
Seokeon Choi, Debasmit Das, Sungha Choi, Seunghan Yang, Hyunsin Park, Sungrack Yun
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023, Accepted (25.8% acceptance rate).
[PDF](C7) TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation
Hyesu Lim, Byeonggeun Kim, Jaegul Choo, and Sungha Choi†
International Conference on Learning Representations (ICLR), 2023, Accepted (31.8% acceptance rate).
[PDF](C6) Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes
Sungha Choi,† Seunghan Yang, Seokeon Choi, and Sungrack Yun
European Conference on Computer Vision (ECCV), 2022, Accepted (28.4% acceptance rate).
[PDF] [TALK1 & DEMO] [TALK2](C5) 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 (*: equal contributions)
International Conference on Computer Vision (ICCV), 2021, Accepted as Oral Presentation (3% acceptance rate).
[PDF](C4) RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening
Sungha Choi,*† Sanghun Jung,* Huiwon Yun, Joanne Kim, Seungryong Kim, and Jaegul Choo (*: equal contributions)
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021, Accepted as Oral Presentation (4.7% acceptance rate).
[PDF] [TALK1] [TALK2](W1) Towards Lightweight Lane Detection by Optimizing Spatial Embedding
Seokwoo Jung,* Sungha Choi,*† Mohammad Azam Khan, Jaegul Choo (*: equal contributions)
European Conference on Computer Vision Workshop on Perception for Autonomous Driving (ECCVW), 2020.
[PDF](C3) Cars Can’t Fly Up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks
Sungha Choi, Joanne Kim, and Jaegul Choo
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020, Seattle, WA (22.1% acceptance rate).
[PDF] [TALK](C2) Image-to-Image Translation via Group-wise Deep Whitening and Coloring
Wonwoong Cho, Sungha Choi, David Park, Inkyu Shin, and Jaegul Choo
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, Long Beach, CA, Accepted as Oral Presentation (5.5% acceptance rate).
[PDF](J1) A New Ensemble Learning Algorithm using Regional Classifiers
Byungwoo Lee, Sungha Choi, Byunghwa Oh, Jihoon Yang, and Sungyong Park
International Journal on Artificial Intelligence Tools 22(4), 2013.(C1) Ensembles of Region-Based Classifiers
Sungha Choi, Byungwoo Lee, and Jihoon Yang
IEEE International Conference on Computer and Information Technology (CIT), 2007, Accepted as Best Paper Award (1st prize among 188 accepted papers)
[PDF]
Patents
23+ U.S. patent applications, with 20 granted (See CV for details)
Research Interests
(Recently active research topics are indicated in boldface)
(Recently active research topics are indicated in boldface)
AI in the Automotive Industry
Generative AI, Multimodal Large Language Models
Fine-tuning or Personalization of Large Vision Foundation Models (LLM, Diffusion, SAM, etc.)
On-Device AI: Test-Time Adaptation (C10, C9, C7, C6), Efficient Training (C9), Network Compression (Pruning and Quantization)
Domain Generalization (C11, C8, C4), (Source-Free) Domain Adaptation
Continual learning (C9), Self-Supervised/Unsupervised Learning (C6), Out-of-Distribution Detection/Anomaly Detection (C5)
Urban-Scene (Semantic/Instance) Segmentation/Detection (C5, C4, C3, W1)
Awards and Honors
Excellent Paper Award, Korea University, 2022
Fully-Funded Scholarship from LG Electronics for My Ph.D Study
Creative People Award, LG Electronics, 2014
Best Paper Award, International Conference on Computer and Information Technology, 2007
Fully-Funded Scholarship from LG Electronics for My M.S Study
Repositories
RobustNet ★210+
An official PyTorch implementation of “RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening” (CVPR 2021 Oral)HANet ★210+
An official PyTorch implementation of “Cars Can’t Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks” (CVPR 2020)
Invited Talks
Tech. Seminar at AI Frontiers Summit hosted by KICS Jul. 2023
Recent Advances on Test-Time Domain Adaptation (30 min)
[TALK]Tech. Seminar at Korea University hosted by Computer Vision Lab., Jan. 2022
Toward Robust Urban-Scene Segmentation via Height-driven Attention Networks and Instance Selective Whitening (1 hour)Tech. Seminar at 42dot, Aug. 2021
My Urban-Scene Segmentation Research (1 hour)Tech. Seminar at AI Retreat, AI Institute, Seoul National University, Apr. 2021
RobustNet: Improving Domain Generalization (10 min)
[TALK]Tech. Seminar at Automotive Electronic Technology Workshop (hosted by Hyundai Motors Company), Sep. 2016
Connected Car Services with Smart Device Connectivity (45 min)Tech. Seminar at the conference hosted by Korean Institute of Electronics and Information Engineers, Jun 2016
Connectivity Technology in the Session of Future Automotive Technology (20 min)