| Meta batch-instance normalization for generalizable person re-identification S Choi, T Kim, M Jeong, H Park, C Kim Proceedings of the IEEE/CVF conference on Computer Vision and Pattern …, 2021 | 176 | 2021 |
| Neural architecture search for spiking neural networks Y Kim, Y Li, H Park, Y Venkatesha, P Panda European conference on computer vision, 36-56, 2022 | 115 | 2022 |
| Neuromorphic data augmentation for training spiking neural networks Y Li, Y Kim, H Park, T Geller, P Panda European Conference on Computer Vision, 631-649, 2022 | 107 | 2022 |
| Robust federated learning with noisy labels S Yang, H Park, J Byun, C Kim IEEE Intelligent Systems 37 (2), 35-43, 2022 | 94 | 2022 |
| Rate coding or direct coding: Which one is better for accurate, robust, and energy-efficient spiking neural networks? Y Kim, H Park, A Moitra, A Bhattacharjee, Y Venkatesha, P Panda ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 90 | 2022 |
| Exploring lottery ticket hypothesis in spiking neural networks Y Kim, Y Li, H Park, Y Venkatesha, R Yin, P Panda European Conference on Computer Vision, 102-120, 2022 | 60 | 2022 |
| Binding touch to everything: Learning unified multimodal tactile representations F Yang, C Feng, Z Chen, H Park, D Wang, Y Dou, Z Zeng, X Chen, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 38 | 2024 |
| Exploring temporal information dynamics in spiking neural networks Y Kim, Y Li, H Park, Y Venkatesha, A Hambitzer, P Panda Proceedings of the AAAI Conference on Artificial Intelligence 37 (7), 8308-8316, 2023 | 35 | 2023 |
| Wordepth: Variational language prior for monocular depth estimation Z Zeng, D Wang, F Yang, H Park, S Soatto, D Lao, A Wong Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 22 | 2024 |
| Wearable-based human activity recognition with spatio-temporal spiking neural networks Y Li, R Yin, H Park, Y Kim, P Panda arXiv preprint arXiv:2212.02233, 2022 | 14 | 2022 |
| Uncovering the representation of spiking neural networks trained with surrogate gradient Y Li, Y Kim, H Park, P Panda arXiv preprint arXiv:2304.13098, 2023 | 13 | 2023 |
| Test-Time Adaptation for Depth Completion H Park, A Gupta, A Wong Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 12 | 2024 |
| On the viability of monocular depth pre-training for semantic segmentation D Lao, F Yang, D Wang, H Park, S Lu, A Wong, S Soatto European Conference on Computer Vision, 340-357, 2024 | 8 | 2024 |
| Neurobind: Towards unified multimodal representations for neural signals F Yang, C Feng, D Wang, T Wang, Z Zeng, Z Xu, H Park, P Ji, H Zhao, Y Li, ... arXiv preprint arXiv:2407.14020, 2024 | 6 | 2024 |
| Addressing client drift in federated continual learning with adaptive optimization Y Venkatesha, Y Kim, H Park, Y Li, P Panda Available at SSRN 4188586, 2022 | 6 | 2022 |
| Augundo: Scaling up augmentations for monocular depth completion and estimation Y Wu, TY Liu, H Park, S Soatto, D Lao, A Wong European Conference on Computer Vision, 274-293, 2024 | 5 | 2024 |
| Divide-and-conquer the NAS puzzle in resource-constrained federated learning systems Y Venkatesha, Y Kim, H Park, P Panda Neural Networks 168, 569-579, 2023 | 4 | 2023 |
| Augundo: Scaling up augmentations for unsupervised depth completion Y Wu, TY Liu, H Park, S Soatto, D Lao, A Wong arXiv preprint arXiv:2310.09739, 2023 | 4 | 2023 |
| Rsa: Resolving scale ambiguities in monocular depth estimators through language descriptions Z Zeng, Y Wu, H Park, D Wang, F Yang, S Soatto, D Lao, BW Hong, ... arXiv preprint arXiv:2410.02924, 2024 | 3 | 2024 |
| All-day Depth Completion V Ezhov, H Park, Z Zhang, R Upadhyay, H Zhang, CC Chandrappa, ... arXiv preprint arXiv:2405.17315, 2024 | 3 | 2024 |
Youngeun KimResearch Scientist, Meta Reality Labsmeta.com의 이메일 확인됨