Professor

Kwanghoon Sohn, Ph.D. Professor C125, The 3rd Engineering Building, Yonsei University 50 Yonsei-ro, Seodaemun-Gu, Seoul 120-749, Korea Tel: +82-2-2123-2879 Fax: +82-2-313-2879 Email: khsohnyonsei.ac.kr Professor, School of Electrical and Electronic Engineering, Yonsei University Underwood Distinguished Professor, Yonsei University Member, National Accademy of Engineering of Korea

 

Education

• BE degree in electronics engineering from Yonsei University, Seoul, Korea, in 1983
• MSEE degree in electrical engineering from University of Minnesota in 1985
• PhD degree in electrical and computer engineering from North Carolina State University in 1992

Research Interests

• 2D/3D Image Processing
• 2D/3D Computer Vision
• Deep Learning, Convolutional Neural Networks
• Multi-modal/Multi-spectral Computer Vision

Academic Position

• Professor, School of electrical and Electronic Engineering, Yonsei University, Seoul, Korea, 1995.3-present
• Chair, School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, 2012.3-2014.2
• Visiting Professor, School of Computer Engineering, Nanyang Technological University, Singapore, 2002.9-2003.8
• Director, BK21Plus Institute of BEST Information Technology, Yonsei University, Seoul, Korea, 2012.3-2016.8

Selected Publication

In computer vision, the top tier conferences (CVPR, NeurIPS, ICLR, ECCV, ICCV, AAAI) have greater impact than most SCI journals. Oral presentations have an highly competitive acceptance rate of about 4% and poster presentations about 20%. According to the Google Scholar statistics, their H5-index are 422 for CVPR, 309 for NeurIPS, 303 for ICLR, 238 for ECCV, 228 for ICCV, and 212 for AAAI, which correspond to the top 1 - 6 rank of all computer science journals and conferences. Note that CVPR is the only conference proceedings listed in the top 10 publications in Google Scholar and is ranked No.1 in Computer Science and Electrical Engineering. IEEE TPAMI and TIP have among the highest JCR impact factors across all computer science categories, such as 23.6 for TPAMI (top 1.37%) and 10.6 (top 8.9%) for TIP.
• J. Park, J. Lee and K. Sohn. "Bridging Vision and Language Spaces with Assignment Prediction" ICLR, May. 2024.
• H. Kim, J. Lee, S. Park and K. Sohn. "Contextual, Discriminative, and Unbiased Compositional Zero-Shot Learning" ICCV, Oct. 2023.
• J. Jang, J. Park, J. Kim, H. Kwon and K. Sohn. "Knowing Where to Focus: Event-aware Transformer for Video Grounding" ICCV, Oct. 2023.
• H. Kwon, T. Song, S. Jeong, J. Kim, J. Jang and K. Sohn. "Probabilistic Prompt Learning for Dense Prediction" CVPR, June. 2023.
• T. Song, S. Kim and K. Sohn. "Unsupervised Deep Asymmetric Stereo Matching with Spatially-Adaptive Self-Similarity" CVPR, June. 2023.
• J. Park, J. Lee and K. Sohn. "Dual-path Adaptation from Image to Video Transformers" CVPR, June. 2023.
• M. Kim, S. Kim, Jungin Park, S. Park and K. Sohn. "PartMix: Regularization Strategy to Learn Part Discovery for Visible-Infrared Person Re-identification" CVPR, June. 2023.
• H. Choi, H. Lee, W. Song, S. Jeon, K. Sohn and Dongbo Min. "Local-guided Global: Paired Similarity Representation for Visual Reinforcement Learning" CVPR, June. 2023.
• K. Kim, J. Park, J. Lee, D. Min, and K. Sohn, "PointFix: Learning to Fix Domain Bias for Robust Online Stereo Adaptation," ECCV, Oct. 2022.
• H. Lee, H. Choi, K. Sohn and D. Min, "KNN Local Attention for Image Restoration," CVPR, June, 2022.

• J. Park, J. Lee, I. Kim and K. Sohn, "Probabilistic Representations for Video Contrastive Learning," CVPR, June, 2022.

• J. Kim, J. Lee, J. Park, D. Min and K. Sohn, "Pin the Memory: Learning to Generalize Semantic Segmentation," CVPR, June, 2022.

• S. Cho, S. Hong, S. Jeon, Y. Lee, K. Sohn, S. Kim, "Semantic Correspondence with Transformers," NeurIPS, December, 2021.

• S. Joung, S. Kim, M. Kim, I. Kim and K. Sohn, "Learning Canonical 3D Object Representation for Fine-grained Recognition", ICCV, October.2021.

• H. Choi, H. Lee, S. Kim, S. Kim, K. Sohn and D. Min, "Adaptive Confidence Thresholding for Monocular Depth Estimation", ICCV, October.2021.

• S. Jeon, D. Min, S. Kim, K. Sohn, "Mining Better Samples for Contrastive Learning of Temporal Correspondence", CVPR, June, 2021.

• S. Jeong, Y. Kim, E. Lee, K. Sohn, "Memory-guided Unsupervised Image-to-image Translation", CVPR, June, 2021.

• J. Park, J. Lee, K. Sohn, "Bridge to Answer: Structure-aware Graph Interaction Network for Video Question Answering", CVPR, June, 2021.

• J. Lee, S. Chung, S. Kim, H. Kang, K. Sohn, "Looking into Your Speech: Learning Cross-modal Affinity for Audio-visual Speech Separation", CVPR, June, 2021.

• H. Kim, S. Joung, I. Kim, K. Sohn, "Prototype-guided Saliency Feature Learning for Person Search", CVPR, June, 2021.

• M. Kim, S. Joung, S. Kim, J. Park, I. Kim and K. Sohn, "Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation," AAAI, February. 2021.

• H. Jung, H. Jang, N. Ha and K. Sohn, "Deep Low-Contrast Image Enhancement using Structure Tensor Representation," AAAI, February. 2021.

• S. Jeon, D. Min, S. Kim, J. Choe, and K. Sohn, "Guided Semantic Flow," ECCV, Aug. 2020.

• J. Park, J. Lee, I. Kim, and K. Sohn, "SumGraph: Video Summarization via Recursive Graph Modeling,” ECCV, Aug. 2020.

• S. Joung, S. kim, H. kim, M. Kim, I. Kim, J. Cho and K. Sohn, "Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation," CVPR, Jun. 2020.

• W. Song, S. Choi, S. Jeong, and K. Sohn, "Stereoscopic Image Super-Resolution with Stereo Consistent Feature," AAAI, Feb. 2020 (Oral Presentation).

• S. Kim, D. Min, S. Lin, and K. Sohn, "Discrete-Continuous Transformation Matching for Dense Semantic Correspondence," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 42, pp. 59-73, Jan. 2020.

• S. Jeong, S. Kim, K. Park, and K. Sohn, "Learning to Find Unpaired Cross-spectral Correspondences," IEEE Trans. on Image Processing, vol. 28, pp. 5394-5406, Nov. 2019.

• J. Lee, S. Kim, S. Kim, J. Park, and K. Sohn, "Context-Aware Emotion Recognition Networks," ICCV, Oct. 2019.

• S. Jeon, D. Min, S. Kim, and K. Sohn, "Joint Learning of Semantic Alignment and Object Landmark Detection," ICCV, Oct. 2019.

• S. Kim, S. Kim, D. Min, and K. Sohn, "LAF-Net: Locally Adaptive Fusion Networks for Stereo Confidence Estimation," CVPR, June. 2019 (Oral Presentation).

• S. Kim, D. Min, S. Jeong, S. Kim, S. Jeon, and K. Sohn, "Semantic Attribute Matching Networks," CVPR, June. 2019.

• S. Kim, D. Min, S. Kim, and K. Sohn, "Unified Confidence Estimation Networks for Robust Stereo Matching," IEEE Trans. on Image Processing, vol. 28, pp. 1299-1313, Mar. 2019.

• S. Kim, D. Min, B. Ham, S. Lin, and K. Sohn, "FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 41, pp. 581-595, Mar. 2019.

• S. Kim, S. Lin, S. Jeon, D. Min, and K. Sohn, "Recurrent Transformer Networks for Semantic Correspondence," NeurIPS, Dec. 2018 (Spotlight Presentation).

• S. Jeon, S. Kim, D. Min, and K. Sohn, "PARN : Pyramidal Affine Regression Networks for Dense Semnatic Correspondence Estimation," ECCV 2018, Sep. 2018.

• T. Song, Y. Kim, C. Oh, and K. Sohn, "Deep Network for Simultaneous Stereo Matching and Dehazing," BMVC, Sep. 2018 (Awared the Best Science Paper).

• Y. Kim, H. Jung, D. Min, and K. Sohn, "Deep Monocular Depth Estimation via Integration of Global and Local Predictions," IEEE Trans. on Image Processing, vol. 27, pp. 4131-4144, Aug. 2018.

• S. Kim, D. Min, S. Lin and K. Sohn, "DCTM: Discrete-Continuous Transform Matching for Semantic Flow," ICCV 2017, Oct. 2017 (Oral Presentation).

• S. Kim, D. Min, S. Kim, and K. Sohn, "Feature Augmentation for Learning Confidence Measure in Stereo Matching," IEEE Trans. on Image Processing, vol. 26, pp. 6019-6033, Dec. 2017.

• S. Kim, D. Min, B.Ham, S.Jeon, S.Lin and K. Sohn, "FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence," IEEE CVPR 2017, Jul. 2017.

• Y. Kim, H. Jung, D. Min and K. Sohn, "Deeply Aggregated Alternating Minimization for Image Restoration," IEEE CVPR 2017, Jul. 2017 (Spotlight Presentation).

• S. Kim, D. Min, B. Ham, M. Do, and K. Sohn, "DASC: Robust Dense Descriptor for Multi-modal and Multi-spectral Correspondence Estimation," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 39, pp. 1712-1729, Sep. 2017.

• S. Kim, R. Cai, K. Park, S. Kim, and K. Sohn, "Modality-Invariant Image Classification Based on Modality Uniqueness and Dictionary Learning," IEEE Trans. on Image Processing, vol. 26, no. 2, pp. 884-899, Feb. 2017.

• Y. Kim, B. Ham, C. Oh, and K. Sohn, "Structure selective depth super-resolution for RGB-D cameras," IEEE Trans. on Image Processing, vol. 25, no. 11, pp. 5227-38, Nov. 2016.

• S. Kim, K. Park, K. Sohn, and S. Lin, "Unified Depth Prediction and Intrinsic Image Decomposition from a Single Image via Joint Convolutional Neural Fields ," Proc. ECCV 2016, Oct. 2016 (Spotlight Presentation).

• S. Kim, D. Min, S. Lin, and K. Sohn, "Deep Self-Convolutional Activations Descriptor for Dense Cross-Modal Correspondence," Proc. ECCV 2016, Oct. 2016.

• S. Choi, D. Min, B. Ham, Y. Kim, C. Oh, and K. Sohn, "Depth Analogy: Data-driven Approach for Single Image Depth Estimation using Gradient Samples," IEEE Trans. on Image Processing, vol.24, no. 12, pp. 5953-5966, Dec. 2015.

• S. Choi, D. Min, B. Ham, and K. Sohn, "Unsupervised Texture Flow Estimation Using Appearance-space Clustering and Correspondence," IEEE Trans. on Image Processing, vol. 24, no. 11, pp. 3652-3665, Nov. 2015.

• S. Kim, D. Min, B. Ham, S. Ryu, M. Do, and K. Sohn, "DASC: Dense Adaptive Self-Correlation Descriptor for Multi-modal and Multi-spectral Correspondence," CVPR 2015, Jun. 2015.

• B. Ham, D. Min, and K. Sohn, "Depth Super-Resolution by Transduction," IEEE Trans. on Image Processing, vol. 24, no. 5, pp. 1524-1535, May 2015.

• D. Min, S. Choi, J. Lu, B. Ham, K. Sohn, and M. N. Do, "Fast Global Image Smoothing based on Weighted Least Squares," IEEE Trans. on Image Processing, vol. 23, no. 12, pp. 5638-5653, Dec. 2014.

• B. Ham, D. Min, C. Oh, M. Do, and K. Sohn, "Probability-Based Rendering for View Synthesis," IEEE Trans. on Image Processing, vol. 23, no. 2, pp. 870-884, Feb. 2014.

• B. Ham, D. Min, and K. Sohn, "Generalized Random Walk with Restart and Its Application to Depth Up-sampling and Interactive Segmentation," IEEE Trans. on Image Processing, vol. 22, no. 7, pp. 2574-2588, Jul. 2013.

• S. Choi, B. Ham, and K. Sohn, "Space-time Hole Filling with Random Walks in View Extrapolation for 3-D Video," IEEE Trans. on Image Processing, vol. 22, no. 6, pp. 2429-2441, Jun. 2013.

• B. Ham, D. Min, and K. Sohn, "Revisiting the Relationship between Adaptive Smoothing and Anisotropic Diffusion with Modified Filters," IEEE Trans. on Image Processing, vol. 22, no. 3, pp. 1096-1107, Mar. 2013.

• B. Ham, D. Min, and K. Sohn, "A Robust Scale-Space Filter using Second Order Partial Differential Equations," IEEE Trans. on Image Processing, vol. 21, no. 9, pp. 3937-3951, Sep. 2012.

• D. Min and K. Sohn, "Cost aggregation and occlusion handling with WLS in stereo matching," IEEE Trans. on Image Processing, vol. 17, no. 8, pp 1431-1442, Aug. 2008.

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• C129, The 3rd Engineering Building, Yonsei University, 50 Yonsei-ro, Seodaemun-Gu, Seoul 03722, Korea
• Phone: +82-2-2123-2879
  Email: khsohnyonsei.ac.kr

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