Jeongwon Min

According to our database, Jeongwon Min authored at least 7 papers between 2024 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Guided Model Merging for Hybrid Data Learning: Leveraging Centralized Data to Refine Decentralized Models.

Junyi Zhu, Ruicong Yao, Taha Ceritli, Savas Özkan, Matthew B. Blaschko, Eunchung Noh, Jeongwon Min, Cho Jung Min, Mete Ozay

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2026

2025

Unlocking the Value of Decentralized Data: A Federated Dual Learning Approach for Model Aggregation.

Junyi Zhu, Ruicong Yao, Taha Ceritli, Savas Özkan, Matthew B. Blaschko, Eunchung Noh, Jeongwon Min, Cho Jung Min, Mete Ozay

CoRR, March, 2025

Efficient and Accurate Scene Text Recognition with Cascaded-Transformers.

Savas Özkan, Andrea Maracani, Mete Ozay, Hyowon Kim, Sijun Cho, Eunchung Noh, Jeongwon Min, Jung Min Cho

Proceedings of the 16th ACM Multimedia Systems Conference, 2025

Hyper-Refinement for Low-Rank Adaptation.

Savas Özkan, Taha Ceritli, Jeongwon Min, Eunchung Noh, Jung Min Cho, Dookun Park, Mete Ozay

Proceedings of the 2025 IEEE International Conference on Acoustics, 2025

A Study of Improving The Privacy-Utility Trade-off of Task-specific Models with Learnable Privacy.

Savas Özkan, Taha Ceritli, Jeongwon Min, Eunchung Noh, Jung Min Cho, Dookun Park, Mete Ozay

Proceedings of the 2025 IEEE International Conference on Acoustics, 2025

Accurate Scene Text Recognition with Efficient Model Scaling and Cloze Self-Distillation.

Andrea Maracani, Savas Özkan, Sijun Cho, Hyowon Kim, Eunchung Noh, Jeongwon Min, Cho Jung Min, Dookun Park, Mete Ozay

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

2024

A Study of Parameter Efficient Fine-tuning by Learning to Efficiently Fine-Tune.

Taha Ceritli, Savas Özkan, Jeongwon Min, Eunchung Noh, Cho Min, Mete Ozay

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024