10 citations · 19 across the 6 of their papers we have counts for
6 papers
FREST: Feature RESToration for Semantic Segmentation under Multiple Adverse Conditions
Sohyun Lee, Namyup Kim, Sungyeon Kim +1
Robust semantic segmentation under adverse conditions is crucial in real-world applications. To address this challenging task in practical scenarios where labeled normal condition…
Shatter and Gather: Learning Referring Image Segmentation with Text Supervision
Dongwon Kim, Namyup Kim, Cuiling Lan +1
Referring image segmentation, the task of segmenting any arbitrary entities described in free-form texts, opens up a variety of vision applications. However, manual labeling of tra…
Learning to Detect Semantic Boundaries with Image-level Class Labels
Namyup Kim, Sehyun Hwang, Suha Kwak
This paper presents the first attempt to learn semantic boundary detection using image-level class labels as supervision. Our method starts by estimating coarse areas of object cla…
Improving Cross-Modal Retrieval with Set of Diverse Embeddings
Dongwon Kim, Namyup Kim, Suha Kwak
Cross-modal retrieval across image and text modalities is a challenging task due to its inherent ambiguity: An image often exhibits various situations, and a caption can be coupled…
ReSTR: Convolution-free Referring Image Segmentation Using Transformers
Namyup Kim, Dongwon Kim, Cuiling Lan +2
Referring image segmentation is an advanced semantic segmentation task where target is not a predefined class but is described in natural language. Most of existing methods for thi…
WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation
Namyup Kim, Taeyoung Son, Jaehyun Pahk +3
Domain generalization for semantic segmentation is highly demanded in real applications, where a trained model is expected to work well in previously unseen domains. One challenge…