1 code implementation • ECCV 2020 • Wonho Bae, Junhyug Noh, Gunhee Kim
Weakly supervised object localization (WSOL) is a task of localizing an object in an image only using image-level labels.
no code implementations • 16 Jul 2024 • Wonho Bae, Junhyug Noh, Danica J. Sutherland
The ProbCover method of Yehuda et al. is a well-motivated algorithm for active learning in low-budget regimes, which attempts to "cover" the data distribution with balls of a given radius at selected data points.
1 code implementation • 25 Jun 2024 • Youngmin Kim, Saejin Kim, Hoyeon Moon, Youngjae Yu, Junhyug Noh
To address these issues, we propose ScalpVision, an AI-driven system for the holistic diagnosis of scalp diseases and alopecia.
1 code implementation • 16 Aug 2022 • Jinhwan Seo, Wonho Bae, Danica J. Sutherland, Junhyug Noh, Daijin Kim
Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations.
Ranked #1 on
Weakly Supervised Object Detection
on MS-COCO-2017
no code implementations • 2 May 2022 • Wonho Bae, Junhyug Noh, Milad Jalali Asadabadi, Danica J. Sutherland
Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model to identify objects in images based on a small number of images with pixel-level labels, and many more images with only image-level labels.
1 code implementation • 15 Jul 2021 • Sangmin Woo, Junhyug Noh, Kangil Kim
TSPN tells when to look: it simultaneously predicts start-end timestamps (i. e., temporal spans) and categories of the all possible relations by utilizing full video context.
Ranked #2 on
Video Visual Relation Detection
on ImageNet-VidVRD
1 code implementation • 16 Jun 2021 • Sangmin Woo, Junhyug Noh, Kangil Kim
To quantify how much LOGIN is aware of relational direction, a new diagnostic task called Bidirectional Relationship Classification (BRC) is also proposed.
Ranked #1 on
Predicate Classification
on Visual Genome
(R@100 metric)
Bidirectional Relationship Classification
Graph Generation
+4
no code implementations • 17 Oct 2020 • Yunchao Wei, Shuai Zheng, Ming-Ming Cheng, Hang Zhao, LiWei Wang, Errui Ding, Yi Yang, Antonio Torralba, Ting Liu, Guolei Sun, Wenguan Wang, Luc van Gool, Wonho Bae, Junhyug Noh, Jinhwan Seo, Gunhee Kim, Hao Zhao, Ming Lu, Anbang Yao, Yiwen Guo, Yurong Chen, Li Zhang, Chuangchuang Tan, Tao Ruan, Guanghua Gu, Shikui Wei, Yao Zhao, Mariia Dobko, Ostap Viniavskyi, Oles Dobosevych, Zhendong Wang, Zhenyuan Chen, Chen Gong, Huanqing Yan, Jun He
The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in developing novel approaches that would harness the imperfect data and improve the data-efficiency during training.
no code implementations • ICCV 2019 • Junhyug Noh, Wonho Bae, Wonhee Lee, Jinhwan Seo, Gunhee Kim
In spite of recent success of proposal-based CNN models for object detection, it is still difficult to detect small objects due to the limited and distorted information that small region of interests (RoI) contain.
no code implementations • CVPR 2018 • Junhyug Noh, Soochan Lee, Beomsu Kim, Gunhee Kim
We propose methods of addressing two critical issues of pedestrian detection: (i) occlusion of target objects as false negative failure, and (ii) confusion with hard negative examples like vertical structures as false positive failure.