no code implementations • 28 Feb 2024 • Dongyoung Kim, Jinwoo Kim, Junsang Yu, Seon Joo Kim
White balance (WB) algorithms in many commercial cameras assume single and uniform illumination, leading to undesirable results when multiple lighting sources with different chromaticities exist in the scene.
1 code implementation • 8 Dec 2023 • Hanjung Kim, Jaehyun Kang, Miran Heo, Sukjun Hwang, Seoung Wug Oh, Seon Joo Kim
By effectively resolving the over-reliance on location information, we achieve state-of-the-art results on YouTube-VIS 2019/2021 and Occluded VIS (OVIS).
no code implementations • 13 Oct 2023 • Jinwoo Kim, Janghyuk Choi, Jaehyun Kang, Changyeon Lee, Ho-Jin Choi, Seon Joo Kim
The binding problem in artificial neural networks is actively explored with the goal of achieving human-level recognition skills through the comprehension of the world in terms of symbol-like entities.
no code implementations • 20 Aug 2023 • Hyunbo Shim, In Cho, Daekyu Kwon, Seon Joo Kim
This paper presents a novel optimization-based method for non-line-of-sight (NLOS) imaging that aims to reconstruct hidden scenes under various setups.
1 code implementation • CVPR 2023 • Jinwoo Kim, Janghyuk Choi, Ho-Jin Choi, Seon Joo Kim
Object-centric learning (OCL) aspires general and compositional understanding of scenes by representing a scene as a collection of object-centric representations.
1 code implementation • ICCV 2023 • Joungbin An, Hyolim Kang, Su Ho Han, Ming-Hsuan Yang, Seon Joo Kim
Online Action Detection (OAD) is the task of identifying actions in streaming videos without access to future frames.
Ranked #1 on Online Action Detection on TVSeries
1 code implementation • NeruIPS 2022 • Sejong Yang, Subin Jeon, Seonghyeon Nam, Seon Joo Kim
There are three main obstacles for interspecies face understanding: (1) lack of animal data compared to human, (2) ambiguous connection between faces of various animals, and (3) extreme shape and style variance.
1 code implementation • 17 Nov 2022 • Lee Hyun, Taehyun Kim, Hyolim Kang, Minjoo Ki, Hyeonchan Hwang, Kwanho Park, Sharang Han, Seon Joo Kim
Commercial adoption of automatic music composition requires the capability of generating diverse and high-quality music suitable for the desired context (e. g., music for romantic movies, action games, restaurants, etc.).
1 code implementation • CVPR 2023 • Miran Heo, Sukjun Hwang, Jeongseok Hyun, Hanjung Kim, Seoung Wug Oh, Joon-Young Lee, Seon Joo Kim
Notably, we greatly outperform the state-of-the-art on the long VIS benchmark (OVIS), improving 5. 6 AP with ResNet-50 backbone.
Ranked #6 on Video Instance Segmentation on YouTube-VIS 2021 (using extra training data)
1 code implementation • CVPR 2023 • Hyolim Kang, Hanjung Kim, Joungbin An, Minsu Cho, Seon Joo Kim
Temporal Action Localization (TAL) methods typically operate on top of feature sequences from a frozen snippet encoder that is pretrained with the Trimmed Action Classification (TAC) tasks, resulting in a task discrepancy problem.
no code implementations • 21 Jul 2022 • Jaeyeon Kang, Seoung Wug Oh, Seon Joo Kim
The key to video inpainting is to use correlation information from as many reference frames as possible.
1 code implementation • 9 Jun 2022 • Miran Heo, Sukjun Hwang, Seoung Wug Oh, Joon-Young Lee, Seon Joo Kim
Specifically, we use an image object detector as a means of distilling object-specific contexts into object tokens.
Ranked #11 on Video Instance Segmentation on YouTube-VIS 2021 (using extra training data)
1 code implementation • CVPR 2022 • Sukjun Hwang, Miran Heo, Seoung Wug Oh, Seon Joo Kim
The set classifier is plug-and-playable to existing object trackers, and highly improves the performance of long-tailed object tracking.
no code implementations • CVPR 2022 • Hyolim Kang, Jinwoo Kim, Taehyun Kim, Seon Joo Kim
Generic Event Boundary Detection (GEBD) is a newly suggested video understanding task that aims to find one level deeper semantic boundaries of events.
1 code implementation • CVPR 2022 • Su Ho Han, Sukjun Hwang, Seoung Wug Oh, Yeonchool Park, Hyunwoo Kim, Min-Jung Kim, Seon Joo Kim
We also introduce cooperatively operating modules that aggregate information from available frames, in order to enrich the features for all subtasks in VIS.
no code implementations • 29 Nov 2021 • Hyolim Kang, Jinwoo Kim, Taehyun Kim, Seon Joo Kim
Generic Event Boundary Detection (GEBD) is a newly suggested video understanding task that aims to find one level deeper semantic boundaries of events.
1 code implementation • 22 Jun 2021 • Hyolim Kang, Jinwoo Kim, KyungMin Kim, Taehyun Kim, Seon Joo Kim
Generic Event Boundary Detection (GEBD) is a newly introduced task that aims to detect "general" event boundaries that correspond to natural human perception.
1 code implementation • CVPR 2021 • Younghyun Jo, Seon Joo Kim
We train a deep SR network with a small receptive field and transfer the output values of the learned deep model to the LUT.
1 code implementation • CVPR 2021 • Younghyun Jo, Seoung Wug Oh, Peter Vajda, Seon Joo Kim
By the one-to-many nature of the super-resolution (SR) problem, a single low-resolution (LR) image can be mapped to many high-resolution (HR) images.
Ranked #3 on Blind Super-Resolution on DIV2KRK - 4x upscaling
1 code implementation • NeurIPS 2021 • Sukjun Hwang, Miran Heo, Seoung Wug Oh, Seon Joo Kim
We propose a novel end-to-end solution for video instance segmentation (VIS) based on transformers.
Ranked #31 on Video Instance Segmentation on YouTube-VIS validation
1 code implementation • CVPR 2021 • Gunhee Nam, Miran Heo, Seoung Wug Oh, Joon-Young Lee, Seon Joo Kim
Since the existing datasets are not suitable to validate our method, we build a new polygonal point set tracking dataset and demonstrate the superior performance of our method over the baselines and existing contour-based VOS methods.
1 code implementation • 16 Apr 2021 • Young Hwi Kim, Seonghyeon Nam, Seon Joo Kim
Many video understanding tasks work in the offline setting by assuming that the input video is given from the start to the end.
no code implementations • ICCV 2021 • Hyolim Kang, KyungMin Kim, Yumin Ko, Seon Joo Kim
Temporal action localization has been one of the most popular tasks in video understanding, due to the importance of detecting action instances in videos.
1 code implementation • ICCV 2021 • Dongyoung Kim, Jinwoo Kim, Seonghyeon Nam, Dongwoo Lee, Yeonkyung Lee, Nahyup Kang, Hyong-Euk Lee, ByungIn Yoo, Jae-Joon Han, Seon Joo Kim
Images in our dataset are mostly captured with illuminants existing in the scene, and the ground truth illumination is computed by taking the difference between the images with different illumination combination.
no code implementations • 3 Dec 2020 • Sukjun Hwang, Seoung Wug Oh, Seon Joo Kim
Panoptic segmentation, which is a novel task of unifying instance segmentation and semantic segmentation, has attracted a lot of attention lately.
no code implementations • ECCV 2020 • Subin Jeon, Seonghyeon Nam, Seoung Wug Oh, Seon Joo Kim
To reduce the training-testing discrepancy of the self-supervised learning, a novel cross-identity training scheme is additionally introduced.
no code implementations • 3 May 2020 • Kai Zhang, Shuhang Gu, Radu Timofte, Taizhang Shang, Qiuju Dai, Shengchen Zhu, Tong Yang, Yandong Guo, Younghyun Jo, Sejong Yang, Seon Joo Kim, Lin Zha, Jiande Jiang, Xinbo Gao, Wen Lu, Jing Liu, Kwangjin Yoon, Taegyun Jeon, Kazutoshi Akita, Takeru Ooba, Norimichi Ukita, Zhipeng Luo, Yuehan Yao, Zhenyu Xu, Dongliang He, Wenhao Wu, Yukang Ding, Chao Li, Fu Li, Shilei Wen, Jianwei Li, Fuzhi Yang, Huan Yang, Jianlong Fu, Byung-Hoon Kim, JaeHyun Baek, Jong Chul Ye, Yuchen Fan, Thomas S. Huang, Junyeop Lee, Bokyeung Lee, Jungki Min, Gwantae Kim, Kanghyu Lee, Jaihyun Park, Mykola Mykhailych, Haoyu Zhong, Yukai Shi, Xiaojun Yang, Zhijing Yang, Liang Lin, Tongtong Zhao, Jinjia Peng, Huibing Wang, Zhi Jin, Jiahao Wu, Yifu Chen, Chenming Shang, Huanrong Zhang, Jeongki Min, Hrishikesh P. S, Densen Puthussery, Jiji C. V
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.
1 code implementation • ECCV 2020 • Jaeyeon Kang, Younghyun Jo, Seoung Wug Oh, Peter Vajda, Seon Joo Kim
Video super-resolution (VSR) and frame interpolation (FI) are traditional computer vision problems, and the performance have been improving by incorporating deep learning recently.
no code implementations • 20 Mar 2020 • Younghyun Jo, Jaeyeon Kang, Seoung Wug Oh, Seonghyeon Nam, Peter Vajda, Seon Joo Kim
Our framework is similar to GANs in that we iteratively train two networks - a generator and a loss network.
no code implementations • 20 Mar 2020 • Gunhee Nam, Seoung Wug Oh, Joon-Young Lee, Seon Joo Kim
We propose a novel memory-based tracker via part-level dense memory and voting-based retrieval, called DMV.
1 code implementation • NeurIPS 2019 • Yunji Kim, Seonghyeon Nam, In Cho, Seon Joo Kim
To generate future frames, we first detect keypoints of a moving object and predict future motion as a sequence of keypoints.
1 code implementation • ICCV 2019 • Sungho Lee, Seoung Wug Oh, DaeYeun Won, Seon Joo Kim
We propose a novel DNN-based framework called the Copy-and-Paste Networks for video inpainting that takes advantage of additional information in other frames of the video.
Ranked #6 on Video Inpainting on DAVIS
1 code implementation • ICCV 2019 • Seoung Wug Oh, Sungho Lee, Joon-Young Lee, Seon Joo Kim
Given a set of reference images and a target image with holes, our network fills the hole by referring the contents in the reference images.
1 code implementation • CVPR 2019 • Seoung Wug Oh, Joon-Young Lee, Ning Xu, Seon Joo Kim
We propose a new multi-round training scheme for the interactive video object segmentation so that the networks can learn how to understand the user's intention and update incorrect estimations during the training.
Ranked #6 on Interactive Video Object Segmentation on DAVIS 2017 (AUC-J metric)
3 code implementations • ICCV 2019 • Seoung Wug Oh, Joon-Young Lee, Ning Xu, Seon Joo Kim
In our framework, the past frames with object masks form an external memory, and the current frame as the query is segmented using the mask information in the memory.
Ranked #4 on Interactive Video Object Segmentation on DAVIS 2017 (using extra training data)
no code implementations • CVPR 2019 • Seonghyeon Nam, Chongyang Ma, Menglei Chai, William Brendel, Ning Xu, Seon Joo Kim
Time-lapse videos usually contain visually appealing content but are often difficult and costly to create.
no code implementations • NeurIPS 2018 • Seonghyeon Nam, Yunji Kim, Seon Joo Kim
Our task aims to semantically modify visual attributes of an object in an image according to the text describing the new visual appearance.
no code implementations • ECCV 2018 • Minho Shim, Young Hwi Kim, Kyung-Min Kim, Seon Joo Kim
A major obstacle in teaching machines to understand videos is the lack of training data, as creating temporal annotations for long videos requires a huge amount of human effort.
2 code implementations • CVPR 2018 • Seoung Wug Oh, Joon-Young Lee, Kalyan Sunkavalli, Seon Joo Kim
We validate our method on four benchmark sets that cover single and multiple object segmentation.
1 code implementation • CVPR 2018 • Younghyun Jo, Seoung Wug Oh, Jaeyeon Kang, Seon Joo Kim
We propose a novel end-to-end deep neural network that generates dynamic upsampling filters and a residual image, which are computed depending on the local spatio-temporal neighborhood of each pixel to avoid explicit motion compensation.
Ranked #6 on Video Super-Resolution on Vid4 - 4x upscaling
2 code implementations • CVPR 2018 • Changha Shin, Hae-Gon Jeon, Youngjin Yoon, In So Kweon, Seon Joo Kim
Light field cameras capture both the spatial and the angular properties of light rays in space.
no code implementations • ICCV 2017 • Seonghyeon Nam, Seon Joo Kim
Often called as the radiometric calibration, the process of recovering RAW images from processed images (JPEG format in the sRGB color space) is essential for many computer vision tasks that rely on physically accurate radiance values.
no code implementations • 26 Jun 2017 • Seonghyeon Nam, Seon Joo Kim
Also, spatially varying photo adjustment methods have been studied by exploiting high-level features and semantic label maps.
1 code implementation • 22 May 2017 • Hye-Rin Kim, Yeong-Seok Kim, Seon Joo Kim, In-Kwon Lee
In this paper, we focus on two high level features, the object and the background, and assume that the semantic information of images is a good cue for predicting emotion.
no code implementations • 29 Aug 2016 • Seoung Wug Oh, Seon Joo Kim
Computational color constancy refers to the problem of computing the illuminant color so that the images of a scene under varying illumination can be normalized to an image under the canonical illumination.
no code implementations • CVPR 2016 • Seoung Wug Oh, Michael S. Brown, Marc Pollefeys, Seon Joo Kim
In particular, due to the differences in spectral sensitivities of the cameras, different cameras yield different RGB measurements for the same spectral signal.
no code implementations • CVPR 2016 • Seonghyeon Nam, Youngbae Hwang, Yasuyuki Matsushita, Seon Joo Kim
Modelling and analyzing noise in images is a fundamental task in many computer vision systems.
no code implementations • ICCV 2015 • Hae-Gon Jeon, Joon-Young Lee, Yudeog Han, Seon Joo Kim, In So Kweon
In this paper, we present a novel multi-image motion deblurring method utilizing the coded exposure technique.
1 code implementation • Pacific Graphics 2014 • Rang Nguyen, Seon Joo Kim, Michael S. Brown
Our method is unique in its considera- tion of the scene illumination and the constraint that the mapped image must be within the color gamut of the target image.
no code implementations • CVPR 2014 • Youngbae Hwang, Joon-Young Lee, In So Kweon, Seon Joo Kim
This paper introduces a new color transfer method which is a process of transferring color of an image to match the color of another image of the same scene.