Joshua Tian Jin Tee

According to our database, Joshua Tian Jin Tee authored at least 7 papers between 2022 and 2024.

Collaborative distances:

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

2024

Physics Informed Distillation for Diffusion Models.

Joshua Tian Jin Tee, Kang Zhang, Hee Suk Yoon, Dhananjaya N. Gowda, Chanwoo Kim, Chang D. Yoo

Trans. Mach. Learn. Res., 2024

C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion.

Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee, Mark A. Hasegawa-Johnson, Yingzhen Li, Chang D. Yoo

Proceedings of the Twelfth International Conference on Learning Representations, 2024

BI-MDRG: Bridging Image History in Multimodal Dialogue Response Generation.

Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee, Kang Zhang, Yu-Jung Heo, Du-Seong Chang, Chang D. Yoo

Proceedings of the Computer Vision - ECCV 2024, 2024

2023

DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution.

Axi Niu, Kang Zhang, Joshua Tian Jin Tee, Trung X. Pham, Jinqiu Sun, Chang D. Yoo, In So Kweon, Yanning Zhang

CoRR, 2023

Self-Supervised Visual Representation Learning via Residual Momentum.

Trung Xuan Pham, Axi Niu, Kang Zhang, Joshua Tian Jin Tee, Ji Woo Hong, Chang D. Yoo

IEEE Access, 2023

ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure.

Hee Suk Yoon, Joshua Tian Jin Tee, Eunseop Yoon, Sunjae Yoon, Gwangsu Kim, Yingzhen Li, Chang D. Yoo

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Self-Supervised Visual Representation Learning via Residual Momentum.

Trung X. Pham, Axi Niu, Kang Zhang, Sultan Rizky Hikmawan Madjid, Ji Woo Hong, Daehyeok Kim, Joshua Tian Jin Tee, Chang D. Yoo

CoRR, 2022