Sungha Choi

News

• 7/2023: One paper accepted to ICCV 2023  New!

• 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)

Publications

• (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.
  [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]

Research Interests
(Recently active research topics are indicated in boldface)

• AI in the Automotive Industry

• Generative AI, Personalization of Large Vision Foundation Models (Diffusion, SAM, etc.)

• On-Device AI: Test-Time Adaptation (C10, C9, C7, C6), Efficient Training (C9), Network Compression (Pruning and Quantization)

• Domain Generalization (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)

Repositories

• RobustNet  ★190+
  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)