Sungha Choi

👉 I am looking for self-motivated students (undergraduate / MS / PhD)!

• Feel free to reach out via this email address.

✨ News

• 3/2026: Joined Kyung Hee University and established EMIL (Efficient Multimodal Intelligence Lab) New!

• 2/2026: One paper accepted to CVPR 2026 New!

• 1/2026: One paper accepted to ICLR 2026

• 12/2025: Area Chair at ICLR 2026

• 9/2025: Two papers (main & workshop) accepted to NeurIPS 2025

• 6/2025: One paper accepted to ICCV 2025

• 5/2025: Outstanding Reviewer at CVPR 2025

• 2/2025: One paper accepted to CVPR 2025

• 7/2024: One paper accepted to ECCV 2024

• 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: 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)

Publications
(† Corresponding author or project lead; * Equal contribution)

• (C16) Concept-Aware LoRA for Domain-Aligned Segmentation Dataset Generation
  Minho Park, Sunghyun Park, Jungsoo Lee, Hyojin Park, Kyuwoong Hwang, Fatih Porikli, Jaegul Choo, and Sungha Choi†
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2026, Accepted (25.4% acceptance rate).
  [PDF]

• (C15) FLoC: Facility Location-Based Efficient Visual Token Compression for Long Video Understanding
  Janghoon Cho, Jungsoo Lee, Munawar Hayat, Kyuwoong Hwang, Fatih Porikli, and Sungha Choi†
  International Conference on Learning Representations (ICLR), 2026, Accepted (28% acceptance rate).
  [PDF]

• (C14) Generalized Contrastive Learning for Universal Multimodal Retrieval
  Jungsoo Lee, Janghoon Cho, Hyojin Park, Munawar Hayat, Kyuwoong Hwang, Fatih Porikli, and Sungha Choi†
  Conference on Neural Information Processing Systems (NeurIPS), 2025, Accepted (24.5% acceptance rate).
  [PDF]

• (W2) Think Straight, Stop Smart: Structured Reasoning for Efficient Multi-Hop RAG
  Jihwan Bang, Juntae Lee, Seunghan Yang, and Sungha Choi†
  Neural Information Processing Systems (NeurIPS) Workshop on Efficient Reasoning, 2025
  [PDF]

• (C13) Personalized OVSS: Understanding Personal Concept in Open-Vocabulary Semantic Segmentation
  Sunghyun Park,* Jungsoo Lee,* Shubhankar Borse, Munawar Hayat, Sungha Choi,† Kyuwoong Hwang, and Fatih Porikli
  International Conference on Computer Vision (ICCV), 2025, Accepted (24% acceptance rate).
  [PDF]

• (C12) CustomKD: Customizing Large Vision Foundation for Edge Model Improvement via Knowledge Distillation
  Jungsoo Lee, Debasmit Das, Munawar Hayat, Sungha Choi,† Kyuwoong Hwang, and Fatih Porikli
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025, Accepted (22.1% acceptance rate).
  [PDF]

• (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, and 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, and 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

• 26+ U.S. patent applications, with 20 granted (See CV for details)

Research Areas 🚀

1. 🧿 Efficient Multimodal & Video Understanding

• Long video understanding and temporal reasoning

• Multimodal representation learning across vision, language, and video

• Token compression and efficient inference

2. 🧠 Agentic AI & Reasoning

• Structured and efficient reasoning for small/edge models

• Planning and decision-making in agentic workflows

• Memory-augmented and retrieval-based systems

3. 📱On-Device AI & Edge Intelligence

• Resource-efficient model design and adaptation

• Efficient attention and architectures

• Model compression for resource-constrained environments

4. 🦾 Toward Physical AI

• Bridging perception, reasoning, and action

• Multimodal interaction with real-world environments

• Embodied and real-world AI systems

Bridging Research and Real-World AI 🌍

• Translating research into real-world AI systems

• Enabling on-device AI under practical constraints

• AI application in real-world domains (e.g., Automotive, Robotics, IoT)

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

• (T7) Tech Seminar at Soongsil University, hosted by Prof. Dahuin Jung, Oct. 2024
  Towards Human-Like Adaptation of AI Models (1.5 Hours)

• (T6) Tech Seminar at AI Frontiers Summit, hosted by KICS Jul. 2023
  Recent Advances on Test-Time Domain Adaptation (30 min)
  [TALK]

• (T5) 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)

• (T4) Tech Seminar at 42dot,  Aug. 2021
  My Urban-Scene Segmentation Research (1 hour)

• (T3) Tech Seminar at AI Retreat, AI Institute, Seoul National University,  Apr. 2021
  RobustNet: Improving Domain Generalization (10 min)
  [TALK]

• (T2) Tech Seminar at Automotive Electronic Technology Workshop (hosted by Hyundai Motors Company), Sep. 2016
  Connected Car Services with Smart Device Connectivity (45 min)

• (T1) 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)