‪Sanghyun Jo‬ - ‪Google Scholar‬

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Citations	122	122
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202020212022202320241 5 34 59 23

Co-authors

In-Jae YuSamsung ElectronicsVerified email at samsung.com
Kyungsu KimHarvard Medical School and Massachusetts General HospitalVerified email at mgh.harvard.edu
Fei PanResearch Fellow at EECS, University of MichiganVerified email at umich.edu
Eunho YangKAISTVerified email at kaist.ac.kr

Sanghyun Jo

Sanghyun Jo

OGQ

Verified email at ogqcorp.com - Homepage

Weakly Supervised Learning Open-vocabulary Learning Generative AI


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Puzzle-cam: Improved localization via matching partial and full features S Jo, IJ Yu 2021 IEEE international conference on image processing (ICIP), 639-643, 2021	92	2021
RecurSeed and EdgePredictMix: Pseudo-Label Refinement Learning for Weakly Supervised Semantic Segmentation across Single- and Multi-Stage Frameworks S Jo, IJ Yu, K Kim arXiv preprint arXiv:2204.06754, 2022	12*	2022
RecurSeed and CertainMix for Weakly Supervised Semantic Segmentation S Jo, IJ Yu, K Kim arXiv preprint arXiv:2204.06754 1, 2022	10	2022
MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic Segmentation S Jo, IJ Yu, K Kim ICCV 2023, 2023	8	2023
TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias S Jo, S Ryu, S Kim, E Yang, K Kim arXiv preprint arXiv:2404.00384, 2024		2024
DHR: Dual Features-Driven Hierarchical Rebalancing in Inter-and Intra-Class Regions for Weakly-Supervised Semantic Segmentation S Jo, F Pan, IJ Yu, K Kim arXiv preprint arXiv:2404.00380, 2024		2024
Supplementary Material MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic Segmentation S Jo, IJ Yu, K Kim

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