Search Results for author: Donghyeon Cho

Found 24 papers, 11 papers with code

Global-and-Local Relative Position Embedding for Unsupervised Video Summarization

no code implementations ECCV 2020 Yunjae Jung, Donghyeon Cho, Sanghyun Woo, In So Kweon

In order to summarize a content video properly, it is important to grasp the sequential structure of video as well as the long-term dependency between frames.

Computational Efficiency Position +1

CORE-MPI: Consistency Object Removal with Embedding MultiPlane Image

no code implementations CVPR 2024 Donggeun Yoon, Donghyeon Cho

Multiplane image (MPI) is desirable for social media because of its generality but it is complex and computationally expensive making object removal challenging.

Novel View Synthesis Object

Deterministic Guidance Diffusion Model for Probabilistic Weather Forecasting

1 code implementation5 Dec 2023 Donggeun Yoon, Minseok Seo, Doyi Kim, Yeji Choi, Donghyeon Cho

We also introduce and evaluate the Pacific Northwest Windstorm (PNW)-Typhoon weather satellite dataset to verify the effectiveness of DGDM in high-resolution regional forecasting.

Weather Forecasting

DIFu: Depth-Guided Implicit Function for Clothed Human Reconstruction

no code implementations CVPR 2023 Dae-Young Song, HeeKyung Lee, Jeongil Seo, Donghyeon Cho

Beyond the SMPL, which provides skinned parametric human 3D information, in this paper, we propose a new IF-based method, DIFu, that utilizes a projected depth prior containing textured and non-parametric human 3D information.

IFQA: Interpretable Face Quality Assessment

1 code implementation14 Nov 2022 Byungho Jo, Donghyeon Cho, In Kyu Park, Sungeun Hong

Existing face restoration models have relied on general assessment metrics that do not consider the characteristics of facial regions.

Image Quality Assessment

Lightweight Alpha Matting Network Using Distillation-Based Channel Pruning

1 code implementation14 Oct 2022 Donggeun Yoon, Jinsun Park, Donghyeon Cho

Therefore, there has been a demand for a lightweight alpha matting model due to the limited computational resources of commercial portable devices.

Image Matting Semantic Segmentation

Domain Adaptation without Source Data

3 code implementations3 Jul 2020 Youngeun Kim, Donghyeon Cho, Kyeongtak Han, Priyadarshini Panda, Sungeun Hong

Our key idea is to leverage a pre-trained model from the source domain and progressively update the target model in a self-learning manner.

Attribute Domain Adaptation +1

Restore from Restored: Video Restoration with Pseudo Clean Video

no code implementations CVPR 2021 Seunghwan Lee, Donghyeon Cho, Jiwon Kim, Tae Hyun Kim

We analyze the restoration performance of the fine-tuned video denoising networks with the proposed self-supervision-based learning algorithm, and demonstrate that the FCN can utilize recurring patches without requiring accurate registration among adjacent frames.

Denoising Optical Flow Estimation +3

Restore from Restored: Single Image Denoising with Pseudo Clean Image

no code implementations9 Mar 2020 Seunghwan Lee, Dongkyu Lee, Donghyeon Cho, Jiwon Kim, Tae Hyun Kim

However, these methods have limitations in using internal information available in a given test image.

Image Denoising

Fast Adaptation to Super-Resolution Networks via Meta-Learning

1 code implementation ECCV 2020 Seobin Park, Jinsu Yoo, Donghyeon Cho, Jiwon Kim, Tae Hyun Kim

In the training stage, we train the network via meta-learning; thus, the network can quickly adapt to any input image at test time.

Meta-Learning Super-Resolution

Self-Supervised Fast Adaptation for Denoising via Meta-Learning

no code implementations9 Jan 2020 Seunghwan Lee, Donghyeon Cho, Jiwon Kim, Tae Hyun Kim

Under certain statistical assumptions of noise, recent self-supervised approaches for denoising have been introduced to learn network parameters without true clean images, and these methods can restore an image by exploiting information available from the given input (i. e., internal statistics) at test time.

Denoising Meta-Learning

Key Instance Selection for Unsupervised Video Object Segmentation

no code implementations18 Jun 2019 Donghyeon Cho, Sungeun Hong, Sungil Kang, Jiwon Kim

After M-th frame, we select K IDs based on video saliency and frequency of appearance; then only these key IDs are tracked through the remaining frames.

Object Segmentation +3

Discriminative Feature Learning for Unsupervised Video Summarization

1 code implementation24 Nov 2018 Yunjae Jung, Donghyeon Cho, Dahun Kim, Sanghyun Woo, In So Kweon

The proposed variance loss allows a network to predict output scores for each frame with high discrepancy which enables effective feature learning and significantly improves model performance.

Supervised Video Summarization Unsupervised Video Summarization

Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles

no code implementations24 Nov 2018 Dahun Kim, Donghyeon Cho, In So Kweon

Self-supervised tasks such as colorization, inpainting and zigsaw puzzle have been utilized for visual representation learning for still images, when the number of labeled images is limited or absent at all.

Colorization Representation Learning +2

LinkNet: Relational Embedding for Scene Graph

3 code implementations NeurIPS 2018 Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon

In this paper, we present a method that improves scene graph generation by explicitly modeling inter-dependency among the entire object instances.

Graph Generation Scene Graph Generation

Learning Image Representations by Completing Damaged Jigsaw Puzzles

no code implementations6 Feb 2018 Dahun Kim, Donghyeon Cho, Donggeun Yoo, In So Kweon

The recovery of the aforementioned damage pushes the network to obtain robust and general-purpose representations.

Colorization Representation Learning +2

Two-Phase Learning for Weakly Supervised Object Localization

no code implementations ICCV 2017 Dahun Kim, Donghyeon Cho, Donggeun Yoo, In So Kweon

Weakly supervised semantic segmentation and localiza- tion have a problem of focusing only on the most important parts of an image since they use only image-level annota- tions.

Object Segmentation +5

A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus Estimation

1 code implementation CVPR 2017 Jinsun Park, Yu-Wing Tai, Donghyeon Cho, In So Kweon

In this paper, we introduce robust and synergetic hand-crafted features and a simple but efficient deep feature from a convolutional neural network (CNN) architecture for defocus estimation.

Defocus Estimation Image Generation

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