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Sungyeon Kim
Sungyeon Kim
Ph.D.Student at POSTECH
在 postech.ac.kr 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Proxy anchor loss for deep metric learning
S Kim, D Kim, M Cho, S Kwak
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
4482020
Deep metric learning beyond binary supervision
S Kim, M Seo, I Laptev, M Cho, S Kwak
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
1262019
Promptstyler: Prompt-driven style generation for source-free domain generalization
J Cho, G Nam, S Kim, H Yang, S Kwak
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
492023
Embedding transfer with label relaxation for improved metric learning
S Kim, D Kim, M Cho, S Kwak
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021
462021
Cross-domain ensemble distillation for domain generalization
K Lee, S Kim, S Kwak
European Conference on Computer Vision, 1-20, 2022
372022
Combating label distribution shift for active domain adaptation
S Hwang, S Lee, S Kim, J Ok, S Kwak
European Conference on Computer Vision, 549-566, 2022
222022
Self-taught metric learning without labels
S Kim, D Kim, M Cho, S Kwak
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
222022
Hier: Metric learning beyond class labels via hierarchical regularization
S Kim, B Jeong, S Kwak
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
132023
FREST: Feature RESToration for semantic segmentation under multiple adverse conditions
S Lee, N Kim, S Kim, S Kwak
European Conference on Computer Vision, 1-18, 2025
12025
Efficient and Versatile Robust Fine-Tuning of Zero-shot Models
S Kim, B Jeong, D Kim, S Kwak
European Conference on Computer Vision, 440-458, 2025
12025
Universal Metric Learning with Parameter-Efficient Transfer Learning
S Kim, D Kim, S Kwak
arXiv preprint arXiv:2309.08944, 2023
12023
Learning to generate novel classes for deep metric learning
K Lee, S Kim, S Hong, S Kwak
arXiv preprint arXiv:2201.01008, 2022
12022
Embedding Transfer via Smooth Contrastive Loss
S Kim, D Kim, M Cho, S Kwak
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