no code implementations • 2 Aug 2023 • Moon Ye-Bin, Nam Hyeon-Woo, Wonseok Choi, Nayeong Kim, Suha Kwak, Tae-Hyun Oh
Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues.
no code implementations • ICCV 2023 • Moon Ye-Bin, Jisoo Kim, Hongyeob Kim, Kilho Son, Tae-Hyun Oh
Given the hypothesis, TextManiA transfers pre-trained text representation obtained from a well-established large language encoder to a target visual feature space being learned.
no code implementations • 20 Feb 2023 • Moon Ye-Bin, Dongmin Choi, Yongjin Kwon, Junsik Kim, Tae-Hyun Oh
We address a weakly-supervised low-shot instance segmentation, an annotation-efficient training method to deal with novel classes effectively.
1 code implementation • 14 Aug 2022 • Kim Jun-Seong, Kim Yu-Ji, Moon Ye-Bin, Tae-Hyun Oh
Our voxel-based volume rendering pipeline reconstructs HDR radiance fields with only multi-view LDR images taken from varying camera settings in an end-to-end manner and has a fast convergence speed.
no code implementations • 29 Sep 2021 • Dongmin Choi, Moon Ye-Bin, Junsik Kim, Tae-Hyun Oh
We propose the first weakly-supervised few-shot instance segmentation task and a frustratingly simple but strong baseline model, FoxInst.
1 code implementation • ICLR 2022 • Nam Hyeon-Woo, Moon Ye-Bin, Tae-Hyun Oh
We show that pFedPara outperforms competing personalized FL methods with more than three times fewer parameters.