55 citations · 62 across the 9 of their papers we have counts for
10 papers
Few-Shot Pattern Detection via Template Matching and Regression
Eunchan Jo, Dahyun Kang, Sanghyun Kim +2
We address the problem of few-shot pattern detection, which aims to detect all instances of a given pattern, typically represented by a few exemplars, from an input image. Although…
Memory-Modular Classification: Learning to Generalize with Memory Replacement
Dahyun Kang, Ahmet Iscen, Eunchan Jo +3
We propose a novel memory-modular learner for image classification that separates knowledge memorization from reasoning. Our model enables effective generalization to new classes b…
In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation
Dahyun Kang, Minsu Cho
We present lazy visual grounding, a two-stage approach of unsupervised object mask discovery followed by object grounding, for open-vocabulary semantic segmentation. Plenty of the…
Contrastive Mean-Shift Learning for Generalized Category Discovery
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 t…
Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation
Dahyun Kang, Piotr Koniusz, Minsu Cho +1
We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a Vision Transformer (ViT) pretrained with self-supervision. Our proposed met…
Few-shot Metric Learning: Online Adaptation of Embedding for Retrieval
Deunsol Jung, Dahyun Kang, Suha Kwak +1
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. Despite r…
Integrative Few-Shot Learning for Classification and Segmentation
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…
Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation
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. In…
Relational Embedding for Few-Shot Classification
Dahyun Kang, Heeseung Kwon, Juhong Min +1
We propose to address the problem of few-shot classification by meta-learning "what to observe" and "where to attend" in a relational perspective. Our method leverages relational p…
Hypercorrelation Squeeze for Few-Shot Segmentation
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. This challenging task r…