| Large language models are temporal and causal reasoners for video question answering D Ko, JS Lee, W Kang, B Roh, HJ Kim The 2023 Conference on Empirical Methods in Natural Language Processing, 2023 | 21 | 2023 |
| Video-text representation learning via differentiable weak temporal alignment D Ko, J Choi, J Ko, S Noh, KW On, ES Kim, HJ Kim Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 21 | 2022 |
| Meltr: Meta loss transformer for learning to fine-tune video foundation models D Ko, J Choi, HK Choi, KW On, B Roh, HJ Kim Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 15 | 2023 |
| Randomly shuffled convolution for self-supervised representation learning Y Oh, M Jeon, D Ko, HJ Kim Information Sciences 623, 206-219, 2023 | 2 | 2023 |
| Open-Vocabulary Video Question Answering: A New Benchmark for Evaluating the Generalizability of Video Question Answering Models D Ko, JS Lee, M Choi, J Chu, J Park, HJ Kim Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 2 | 2023 |
| Answer Me if You Can: Debiasing Video Question Answering via Answering Unanswerable Questions D Ko, H Won, J Kim, C Miso, B Roh, HJ Kim | 1 | 2023 |
| Search-and-attack: temporally sparse adversarial perturbations on videos H Heo, D Ko, J Lee, Y Hong, HJ Kim IEEE Access 9, 146938-146947, 2021 | 1 | 2021 |
| MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models (Supplementary Materials) D Ko, J Choi, HK Choi, KW On, B Roh, HJ Kim | | |
| Video-Text Representation Learning via Differentiable Weak Temporal Alignment (Supplement) D Ko, J Choi, J Ko, S Noh, KW On, ES Kim, HJ Kim | | |
Hyunwoo J. KimKorea University在 korea.ac.kr 的电子邮件经过验证
