| Railroad is not a train: Saliency as pseudo-pixel supervision for weakly supervised semantic segmentation S Lee, M Lee, J Lee, H Shim Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 285 | 2021 |
| Evaluating weakly supervised object localization methods right J Choe, SJ Oh, S Lee, S Chun, Z Akata, H Shim Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 228 | 2020 |
| Evaluation for weakly supervised object localization: Protocol, metrics, and datasets J Choe, SJ Oh, S Chun, S Lee, Z Akata, H Shim IEEE transactions on pattern analysis and machine intelligence 45 (2), 1732-1748, 2022 | 33 | 2022 |
| Saliency as pseudo-pixel supervision for weakly and semi-supervised semantic segmentation M Lee, S Lee, J Lee, H Shim IEEE transactions on pattern analysis and machine intelligence 45 (10 …, 2023 | 15 | 2023 |
| Learning from better supervision: Self-distillation for learning with noisy labels K Baek, S Lee, H Shim 2022 26th International Conference on Pattern Recognition (ICPR), 1829-1835, 2022 | 6 | 2022 |
| Weakly Supervised Semantic Segmentation for Driving Scenes D Kim, S Lee, J Choe, H Shim Proceedings of the AAAI Conference on Artificial Intelligence 38 (3), 2741-2749, 2024 | 4 | 2024 |
| Understanding Multi-Granularity for Open-Vocabulary Part Segmentation J Choi, S Lee, S Lee, M Lee, H Shim NeurIPS (Conference on Neural Information Processing Systems), 2024 | 1 | 2024 |
| Fine-Grained Image-Text Correspondence with Cost Aggregation for Open-Vocabulary Part Segmentation J Choi, S Lee, M Lee, S Lee, H Shim arXiv preprint arXiv:2501.09688, 2025 | | 2025 |
| MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation M Lee, S Lee, S Park, D Han, B Heo, H Shim arXiv preprint arXiv:2411.19067, 2024 | | 2024 |
| Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation S Lee, H Lee, H Shim 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2024 | | 2024 |
| Self-Supervised Vision Transformers Are Efficient Segmentation Learners for Imperfect Labels S Lee, S Kang, H Shim AAAI2024: Edge Intelligence Workshop on Large Language and Vision Model, 2024 | | 2024 |
| Weakly supervised semantic segmentation device and method based on pseudo-masks H Shim, S Lee, M Lee US Patent 11,798,171, 2023 | | 2023 |
| Attention-Based Dropout Layer for Weakly Supervised Single Object Localization and Semantic Segmentation J Choe, S Lee, H Shim IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (12), 4256 …, 2021 | | 2021 |
Hyunjung ShimAssociate Professor, KAIST確認したメール アドレス: kaist.ac.kr