Search Results for author: Changick Kim

Found 35 papers, 13 papers with code

Explore and Match: End-to-End Video Grounding with Transformer

no code implementations25 Jan 2022 Sangmin Woo, Jinyoung Park, Inyong Koo, Sumin Lee, Minki Jeong, Changick Kim

We present a new paradigm named explore-and-match for video grounding, which aims to seamlessly unify two streams of video grounding methods: proposal-based and proposal-free.

Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation

no code implementations8 Nov 2021 Byeongjun Park, Taekyung Kim, Hyojun Go, Changick Kim

In this paper, we propose residual guidance loss that enables the depth estimation network to embed the discriminative feature by transferring the discriminability of auto-encoded features.

Monocular Depth Estimation Self-Supervised Learning

Geometrically Adaptive Dictionary Attack on Face Recognition

no code implementations8 Nov 2021 Junyoung Byun, Hyojun Go, Changick Kim

We apply the GADA strategy to two existing attack methods and show overwhelming performance improvement in the experiments on the LFW and CPLFW datasets.

3D Face Alignment Face Alignment +1

Learning to Discriminate Information for Online Action Detection: Analysis and Application

no code implementations8 Sep 2021 Sumin Lee, Hyunjun Eun, Jinyoung Moon, Seokeon Choi, Yoonhyung Kim, Chanho Jung, Changick Kim

To overcome this problem, we propose a novel recurrent unit, named Information Discrimination Unit (IDU), which explicitly discriminates the information relevancy between an ongoing action and others to decide whether to accumulate the input information.

Action Anticipation Action Detection

Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization

1 code implementation30 Aug 2021 Myung-Joon Kwon, Seung-Hun Nam, In-Jae Yu, Heung-Kyu Lee, Changick Kim

It significantly outperforms traditional and deep neural network-based methods in detecting and localizing tampered regions.

Image Manipulation Image Manipulation Detection

DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes

no code implementations ICCV 2021 Dongki Jung, Jaehoon Choi, Yonghan Lee, Deokhwa Kim, Changick Kim, Dinesh Manocha, Donghwan Lee

We present a novel approach for estimating depth from a monocular camera as it moves through complex and crowded indoor environments, e. g., a department store or a metro station.

3D Reconstruction Depth Estimation

Improving Few-shot Learning with Weakly-supervised Object Localization

no code implementations25 May 2021 Inyong Koo, Minki Jeong, Changick Kim

In this work, we propose a novel framework that generates class representations by extracting features from class-relevant regions of the images.

Few-Shot Learning Metric Learning +1

Semi-Supervised Domain Adaptation via Selective Pseudo Labeling and Progressive Self-Training

no code implementations1 Apr 2021 Yoonhyung Kim, Changick Kim

In SSDA, a small number of labeled target images are given for training, and the effectiveness of those data is demonstrated by the previous studies.

Domain Adaptation Representation Learning

Few-shot Open-set Recognition by Transformation Consistency

1 code implementation CVPR 2021 Minki Jeong, Seokeon Choi, Changick Kim

Based on the transformation consistency, our method measures the difference between the transformed prototypes and a modified prototype set.

Few-Shot Learning Open Set Learning

On the Effectiveness of Small Input Noise for Defending Against Query-based Black-Box Attacks

no code implementations13 Jan 2021 Junyoung Byun, Hyojun Go, Changick Kim

In this paper, we pay attention to an implicit assumption of query-based black-box adversarial attacks that the target model's output exactly corresponds to the query input.

Multi-view Arbitrary Style Transfer

no code implementations1 Jan 2021 Taekyung Kim, Changick Kim

We propose a photometric consistency loss, which directly enforces the geometrically consistent style texture across the view, and a stroke consistency loss, which matches the characteristics and directions of the brushstrokes by aligning the local patches of the corresponding pixels before minimizing feature deviation.

Style Transfer

Just a Few Points Are All You Need for Multi-View Stereo: A Novel Semi-Supervised Learning Method for Multi-View Stereo

no code implementations ICCV 2021 Taekyung Kim, Jaehoon Choi, Seokeon Choi, Dongki Jung, Changick Kim

We generate the spare ground truth of the DTU dataset for evaluation and extensive experiments verify that our SGT-MVSNet outperforms the state-of-the-art MVS methods on the sparse ground truth setting.

3D Reconstruction

Robust Federated Learning with Noisy Labels

1 code implementation3 Dec 2020 Seunghan Yang, Hyoungseob Park, Junyoung Byun, Changick Kim

To solve these problems, we introduce a novel federated learning scheme that the server cooperates with local models to maintain consistent decision boundaries by interchanging class-wise centroids.

Federated Learning Learning with noisy labels

Meta Batch-Instance Normalization for Generalizable Person Re-Identification

1 code implementation CVPR 2021 Seokeon Choi, Taekyung Kim, Minki Jeong, Hyoungseob Park, Changick Kim

To this end, we combine learnable batch-instance normalization layers with meta-learning and investigate the challenging cases caused by both batch and instance normalization layers.

Data Augmentation Domain Generalization +2

Arbitrary Style Transfer using Graph Instance Normalization

no code implementations6 Oct 2020 Dongki Jung, Seunghan Yang, Jaehoon Choi, Changick Kim

Style transfer is the image synthesis task, which applies a style of one image to another while preserving the content.

Domain Adaptation Image-to-Image Translation +2

SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction

1 code implementation6 Oct 2020 Jaehoon Choi, Dongki Jung, Donghwan Lee, Changick Kim

In this paper, we propose SAFENet that is designed to leverage semantic information to overcome the limitations of the photometric loss.

Monocular Depth Estimation Multi-Task Learning

Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation

2 code implementations ECCV 2020 Taekyung Kim, Changick Kim

Finally, the exploration scheme locally aligns features in a class-wise manner complementary to the attraction scheme by selectively aligning unlabeled target features complementary to the perturbation scheme.

Unsupervised Domain Adaptation

Partial Domain Adaptation Using Graph Convolutional Networks

no code implementations16 May 2020 Seunghan Yang, Youngeun Kim, Dongki Jung, Changick Kim

Although existing partial domain adaptation methods effectively down-weigh outliers' importance, they do not consider data structure of each domain and do not directly align the feature distributions of the same class in the source and target domains, which may lead to misalignment of category-level distributions.

Partial Domain Adaptation

Single-view 2D CNNs with Fully Automatic Non-nodule Categorization for False Positive Reduction in Pulmonary Nodule Detection

no code implementations9 Mar 2020 Hyunjun Eun, Daeyeong Kim, Chanho Jung, Changick Kim

Note that, instead of manual categorization requiring the heavy workload of radiologists, we propose to automatically categorize non-nodules based on the autoencoder and k-means clustering.

Learning to Discriminate Information for Online Action Detection

1 code implementation CVPR 2020 Hyunjun Eun, Jinyoung Moon, Jongyoul Park, Chanho Jung, Changick Kim

For online action detection, in this paper, we propose a novel recurrent unit to explicitly discriminate the information relevant to an ongoing action from others.

Action Detection

SRG: Snippet Relatedness-based Temporal Action Proposal Generator

no code implementations26 Nov 2019 Hyunjun Eun, Sumin Lee, Jinyoung Moon, Jongyoul Park, Chanho Jung, Changick Kim

Recent temporal action proposal generation approaches have suggested integrating segment- and snippet score-based methodologies to produce proposals with high recall and accurate boundaries.

Action Detection Temporal Action Proposal Generation

BitNet: Learning-Based Bit-Depth Expansion

1 code implementation10 Oct 2019 Junyoung Byun, Kyujin Shim, Changick Kim

Since insufficient bit-depth may generate annoying false contours or lose detailed visual appearance, bit-depth expansion (BDE) from low bit-depth (LBD) images to high bit-depth (HBD) images becomes more and more important.

SSIM

CNN-based Semantic Segmentation using Level Set Loss

no code implementations2 Oct 2019 Youngeun Kim, Seunghyeon Kim, Taekyung Kim, Changick Kim

Note that each binary image consists of background and regions belonging to a class.

Semantic Segmentation

RPM-Net: Robust Pixel-Level Matching Networks for Self-Supervised Video Object Segmentation

no code implementations29 Sep 2019 Youngeun Kim, Seokeon Choi, Hankyeol Lee, Taekyung Kim, Changick Kim

In this paper, we introduce a self-supervised approach for video object segmentation without human labeled data. Specifically, we present Robust Pixel-level Matching Net-works (RPM-Net), a novel deep architecture that matches pixels between adjacent frames, using only color information from unlabeled videos for training.

Semantic Segmentation Video Object Segmentation +1

Learning to Align Multi-Camera Domains using Part-Aware Clustering for Unsupervised Video Person Re-Identification

no code implementations29 Sep 2019 Youngeun Kim, Seokeon Choi, Taekyung Kim, Sumin Lee, Changick Kim

Since the cost of labeling increases dramatically as the number of cameras increases, it is difficult to apply the re-identification algorithm to a large camera network.

Metric Learning Representation Learning +1

Why Does the VQA Model Answer No?: Improving Reasoning through Visual and Linguistic Inference

no code implementations25 Sep 2019 Seungjun Jung, Junyoung Byun, Kyujin Shim, Changick Kim

Moreover, by modifying the VQA model’s answer through the output of the NLI model, we show that VQA performance increases by 1. 1% from the original model.

Common Sense Reasoning Question Answering +1

A Global-Local Emebdding Module for Fashion Landmark Detection

1 code implementation28 Aug 2019 Sumin Lee, Sungchan Oh, Chanho Jung, Changick Kim

To that end, in this paper, we propose a fashion landmark detection network with a global-local embedding module.

Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation

no code implementations1 Aug 2019 Jaehoon Choi, Minki Jeong, Taekyung Kim, Changick Kim

To learn target discriminative representations, using pseudo-labels is a simple yet effective approach for unsupervised domain adaptation.

Semi-Supervised Image Classification Unsupervised Domain Adaptation

Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal

1 code implementation22 Apr 2019 Seungjun Jung, Muhammad Abul Hasan, Changick Kim

In this paper, we propose a novel algorithm to rectify illumination of the digitized documents by eliminating shading artifacts.

Shadow Removal

Cannot find the paper you are looking for? You can Submit a new open access paper.