| Exploring linearity of deep neural network trained QSM: QSMnet+ W Jung, J Yoon, S Ji, JY Choi, JM Kim, Y Nam, EY Kim, J Lee Neuroimage 211, 116619, 2020 | 76 | 2020 |
| Large Loss Matters in Weakly Supervised Multi-Label Classification Y Kim*, JM Kim*, Z Akata, J Lee CVPR 2022, 2022 | 58 | 2022 |
| Waffling around for Performance: Visual Classification with Random Words and Broad Concepts K Roth*, JM Kim*, A Koepke, O Vinyals, C Schmid, Z Akata ICCV 2023, 2023 | 30 | 2023 |
| Keep CALM and Improve Visual Feature Attribution JM Kim*, J Choe*, Z Akata, SJ Oh ICCV 2021, 2021 | 20 | 2021 |
| Exposing and Mitigating Spurious Correlations for Cross-Modal Retrieval JM Kim, A Koepke, C Schmid, Z Akata CVPRW MULA 2023, 2023 | 19 | 2023 |
| Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification Y Kim, JM Kim, J Jeong, C Schmid, Z Akata, J Lee CVPR 2023, 2023 | 11 | 2023 |
| Sampling-based bayesian inference with gradient uncertainty C Park, JM Kim, SH Ha, J Lee NeurIPS Workshop on Bayesian Deep Learning, 2018, 2018 | 6 | 2018 |
| Distributional Prototypical Methods for Reliable Explanation Space Construction H Joo, JM Kim, H Han, J Lee IEEE Access, 2023 | 1 | 2023 |
| Posterior annealing: Fast calibrated uncertainty for regression U Upadhyay, JM Kim, C Schmidt, B Schölkopf, Z Akata arXiv preprint arXiv:2302.11012, 2023 | 1 | 2023 |
| DataDream: Few-shot Guided Dataset Generation JM Kim*, J Bader*, S Alaniz, C Schmid, Z Akata ECCV 2024, 2024 | | 2024 |
| Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models N Singhi, JM Kim, K Roth, Z Akata ECCV 2024, 2024 | | 2024 |
| REST: Performance Improvement of a Black Box Model via RL-Based Spatial Transformation JM Kim*, H Kim*, C Park*, J Lee AAAI 2020, 2020 | | 2020 |
Zeynep AkataProfessor at TUM and Director at Helmholtz Munich在 helmholtz-munich.de 的电子邮件经过验证
