1 code implementation • 30 Apr 2024 • Amarjeet Kumar, Hongxu Jiang, Muhammad Imran, Cyndi Valdes, Gabriela Leon, Dahyun Kang, Parvathi Nataraj, Yuyin Zhou, Michael D. Weiss, Wei Shao
This module uses the cross-slice attention mechanism to effectively capture 3D spatial information by learning long-range dependencies between the center slice (for segmentation) and its neighboring slices.
no code implementations • 15 Apr 2024 • Sua Choi, Dahyun Kang, Minsu Cho
We address the problem of generalized category discovery (GCD) that aims to partition a partially labeled collection of images; only a small part of the collection is labeled and the total number of target classes is unknown.
no code implementations • CVPR 2023 • Dahyun Kang, Piotr Koniusz, Minsu Cho, Naila Murray
For this mixed setup, we propose to improve the pseudo-labels using a pseudo-label enhancer that was trained using the available ground-truth pixel-level labels.
no code implementations • 14 Nov 2022 • Deunsol Jung, Dahyun Kang, Suha Kwak, Minsu Cho
Metric learning aims to build a distance metric typically by learning an effective embedding function that maps similar objects into nearby points in its embedding space.
1 code implementation • CVPR 2022 • Dahyun Kang, Minsu Cho
We introduce the integrative task of few-shot classification and segmentation (FS-CS) that aims to both classify and segment target objects in a query image when the target classes are given with a few examples.
1 code implementation • 29 Nov 2021 • Jeongbeen Yoon, Dahyun Kang, Minsu Cho
Semi-supervised domain adaptation (SSDA) is to adapt a learner to a new domain with only a small set of labeled samples when a large labeled dataset is given on a source domain.
1 code implementation • ICCV 2021 • Dahyun Kang, Heeseung Kwon, Juhong Min, Minsu Cho
We propose to address the problem of few-shot classification by meta-learning "what to observe" and "where to attend" in a relational perspective.
Ranked #15 on Few-Shot Image Classification on CUB 200 5-way 5-shot
1 code implementation • 4 Apr 2021 • Juhong Min, Dahyun Kang, Minsu Cho
Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class.
Ranked #13 on Few-Shot Semantic Segmentation on FSS-1000 (5-shot)
no code implementations • 1 Jan 2021 • Jeongbeen Yoon, Dahyun Kang, Minsu Cho
Semi-supervised domain adaptation (SSDA) is to adapt a learner to a new domain with only a small set of labeled samples when a large labeled dataset is given on a source domain.
no code implementations • ICCV 2021 • Juhong Min, Dahyun Kang, Minsu Cho
Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class.