1 code implementation • 15 Mar 2023 • Won Jo, Geuntaek Lim, Gwangjin Lee, Hyunwoo Kim, Byungsoo Ko, Yukyung Choi
In content-based video retrieval (CBVR), dealing with large-scale collections, efficiency is as important as accuracy; thus, several video-level feature-based studies have actively been conducted.
Ranked #12 on Video Retrieval on FIVR-200K
no code implementations • 8 Dec 2022 • Byungsoo Ko, Han-Gyu Kim, Byeongho Heo, Sangdoo Yun, Sanghyuk Chun, Geonmo Gu, Wonjae Kim
As ViT groups the channels via a multi-head attention mechanism, grouping the channels by GGeM leads to lower head-wise dependence while amplifying important channels on the activation maps.
no code implementations • 8 Dec 2022 • Young-Jun Lee, Byungsoo Ko, Han-Gyu Kim, Ho-Jin Choi
As sharing images in an instant message is a crucial factor, there has been active research on learning a image-text multi-modal dialogue model.
no code implementations • 5 Oct 2022 • Jon Almazán, Byungsoo Ko, Geonmo Gu, Diane Larlus, Yannis Kalantidis
We address it with the proposed Grappa, an approach that starts from a strong pretrained model, and adapts it to tackle multiple retrieval tasks concurrently, using only unlabeled images from the different task domains.
1 code implementation • 28 Mar 2022 • Byungsoo Ko, Geonmo Gu
This paper is a technical report to share our experience and findings building a Korean and English bilingual multimodal model.
1 code implementation • 16 Dec 2021 • Young Kyun Jang, Geonmo Gu, Byungsoo Ko, Isaac Kang, Nam Ik Cho
To mitigate this issue, data augmentation can be applied during training.
2 code implementations • 1 Jun 2021 • Geonmo Gu, Byungsoo Ko, SeoungHyun Go, Sung-Hyun Lee, Jingeun Lee, Minchul Shin
In this paper, we propose a real-time and light-weight line segment detector for resource-constrained environments named Mobile LSD (M-LSD).
Ranked #6 on Line Segment Detection on York Urban Dataset
2 code implementations • 7 Apr 2021 • Minchul Shin, Yoonjae Cho, Byungsoo Ko, Geonmo Gu
In this paper, we study the compositional learning of images and texts for image retrieval.
Ranked #14 on Image Retrieval on Fashion IQ
1 code implementation • ICCV 2021 • Byungsoo Ko, Geonmo Gu, Han-Gyu Kim
This can be undesirable for DML, where training and test data exhibit entirely different classes.
2 code implementations • 29 Mar 2021 • Geonmo Gu, Byungsoo Ko, Han-Gyu Kim
One of the main purposes of deep metric learning is to construct an embedding space that has well-generalized embeddings on both seen (training) classes and unseen (test) classes.
no code implementations • 26 May 2020 • Yang-Ho Ji, HeeJae Jun, Insik Kim, Jongtack Kim, Youngjoon Kim, Byungsoo Ko, Hyong-Keun Kook, Jingeun Lee, Sangwon Lee, Sanghyuk Park
In this paper, we propose an effective pipeline for clothes retrieval system which has sturdiness on large-scale real-world fashion data.
2 code implementations • CVPR 2020 • Byungsoo Ko, Geonmo Gu
Meanwhile, post-processing techniques, such as query expansion and database augmentation, have proposed the combination of feature points to obtain additional semantic information.
1 code implementation • 31 Jan 2020 • Geonmo Gu, Byungsoo Ko
Secondly, it performs hard negative pair mining within the original and synthetic points to select a more informative negative pair for computing the metric learning loss.
no code implementations • 27 Jul 2019 • Byungsoo Ko, Minchul Shin, Geonmo Gu, HeeJae Jun, Tae Kwan Lee, Youngjoon Kim
Many studies have been performed on metric learning, which has become a key ingredient in top-performing methods of instance-level image retrieval.
7 code implementations • arXiv 2019 • HeeJae Jun, Byungsoo Ko, Youngjoon Kim, Insik Kim, Jongtack Kim
Recent studies in image retrieval task have shown that ensembling different models and combining multiple global descriptors lead to performance improvement.
Ranked #1 on Image Retrieval on CUB-200-2011