no code implementations • 16 Mar 2024 • Hoyoung Kim, Sehyun Hwang, Suha Kwak, Jungseul Ok
Training and validating models for semantic segmentation require datasets with pixel-wise annotations, which are notoriously labor-intensive.
no code implementations • ICCV 2023 • Hoyoung Kim, Minhyeon Oh, Sehyun Hwang, Suha Kwak, Jungseul Ok
Learning semantic segmentation requires pixel-wise annotations, which can be time-consuming and expensive.
no code implementations • 15 Dec 2022 • Namyup Kim, Sehyun Hwang, Suha Kwak
This paper presents the first attempt to learn semantic boundary detection using image-level class labels as supervision.
no code implementations • 13 Aug 2022 • Sehyun Hwang, Sohyun Lee, Sungyeon Kim, Jungseul Ok, Suha Kwak
We consider the problem of active domain adaptation (ADA) to unlabeled target data, of which subset is actively selected and labeled given a budget constraint.
1 code implementation • 22 Jun 2022 • Nayeong Kim, Sehyun Hwang, Sungsoo Ahn, Jaesik Park, Suha Kwak
We propose a new method for training debiased classifiers with no spurious attribute label.