Search Results for author: Chang-Su Kim

Found 42 papers, 25 papers with code

Multi-Loss Rebalancing Algorithm for Monocular Depth Estimation

1 code implementation ECCV 2020 Jae-Han Lee, Chang-Su Kim

To address these issues, we propose the loss rebalancing algorithm that initializes and rebalances the weight for each loss function adaptively in the course of training.

Monocular Depth Estimation

Global and Local Enhancement Networks for Paired and Unpaired Image Enhancement

no code implementations ECCV 2020 Han-Ul Kim, Young Jun Koh, Chang-Su Kim

Especially, we propose a two-stage training scheme based on generative adversarial networks for unpaired learning.

Image Enhancement

PieNet: Personalized Image Enhancement Network

1 code implementation ECCV 2020 Han-Ul Kim, Young Jun Koh, Chang-Su Kim

First, we represent various users' preferences for enhancement as feature vectors in an embedding space, called preference vectors.

Image Enhancement Metric Learning

Recursive Video Lane Detection

1 code implementation ICCV 2023 Dongkwon Jin, Dahyun Kim, Chang-Su Kim

A novel algorithm to detect road lanes in videos, called recursive video lane detector (RVLD), is proposed in this paper, which propagates the state of a current frame recursively to the next frame.

Lane Detection

Versatile Depth Estimator Based on Common Relative Depth Estimation and Camera-Specific Relative-to-Metric Depth Conversion

no code implementations20 Mar 2023 Jinyoung Jun, Jae-Han Lee, Chang-Su Kim

A typical monocular depth estimator is trained for a single camera, so its performance drops severely on images taken with different cameras.

Depth Estimation

Context-Based Trit-Plane Coding for Progressive Image Compression

1 code implementation CVPR 2023 Seungmin Jeon, Kwang Pyo Choi, Youngo Park, Chang-Su Kim

Trit-plane coding enables deep progressive image compression, but it cannot use autoregressive context models.

Image Compression

Continuously Masked Transformer for Image Inpainting

1 code implementation ICCV 2023 Keunsoo Ko, Chang-Su Kim

Through several masked self-attention and mask update (MSAU) layers, we predict initial inpainting results.

Image Inpainting

Applying Eigencontours to PolarMask-Based Instance Segmentation

1 code implementation24 Aug 2022 Wonhui Park, Dongkwon Jin, Chang-Su Kim

Eigencontours are the first data-driven contour descriptors based on singular value decomposition.

Instance Segmentation Segmentation +1

Depth Map Decomposition for Monocular Depth Estimation

1 code implementation23 Aug 2022 Jinyoung Jun, Jae-Han Lee, Chul Lee, Chang-Su Kim

We propose a novel algorithm for monocular depth estimation that decomposes a metric depth map into a normalized depth map and scale features.

Ranked #35 on Monocular Depth Estimation on NYU-Depth V2 (using extra training data)

Monocular Depth Estimation

RD-Optimized Trit-Plane Coding of Deep Compressed Image Latent Tensors

no code implementations25 Mar 2022 Seungmin Jeon, Jae-Han Lee, Chang-Su Kim

DPICT is the first learning-based image codec supporting fine granular scalability.

DPICT: Deep Progressive Image Compression Using Trit-Planes

1 code implementation CVPR 2022 Jae-Han Lee, Seungmin Jeon, Kwang Pyo Choi, Youngo Park, Chang-Su Kim

We propose the deep progressive image compression using trit-planes (DPICT) algorithm, which is the first learning-based codec supporting fine granular scalability (FGS).

Image Compression

IceNet for Interactive Contrast Enhancement

1 code implementation13 Sep 2021 Keunsoo Ko, Chang-Su Kim

A CNN-based interactive contrast enhancement algorithm, called IceNet, is proposed in this work, which enables a user to adjust image contrast easily according to his or her preference.

Low-Light Image Enhancement

Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps

1 code implementation CVPR 2021 Yuk Heo, Yeong Jun Koh, Chang-Su Kim

We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time.

Ranked #2 on Interactive Video Object Segmentation on DAVIS 2017 (using extra training data)

Interactive Segmentation Interactive Video Object Segmentation +3

Learning Multiple Pixelwise Tasks Based on Loss Scale Balancing

1 code implementation ICCV 2021 Jae-Han Lee, Chul Lee, Chang-Su Kim

We propose a novel loss weighting algorithm, called loss scale balancing (LSB), for multi-task learning (MTL) of pixelwise vision tasks.

Multi-Task Learning

Interactive Video Object Segmentation Using Global and Local Transfer Modules

4 code implementations ECCV 2020 Yuk Heo, Yeong Jun Koh, Chang-Su Kim

The global transfer module conveys the segmentation information in an annotated frame to a target frame, while the local transfer module propagates the segmentation information in a temporally adjacent frame to the target frame.

Ranked #3 on Interactive Video Object Segmentation on DAVIS 2017 (using extra training data)

Interactive Video Object Segmentation Segmentation +2

Order Learning and Its Application to Age Estimation

1 code implementation ICLR 2020 Kyungsun Lim, Nyeong-Ho Shin, Young-Yoon Lee, Chang-Su Kim

We propose order learning to determine the order graph of classes, representing ranks or priorities, and classify an object instance into one of the classes.

Age Estimation

Monocular Depth Estimation Using Whole Strip Masking and Reliability-Based Refinement

no code implementations ECCV 2018 Minhyeok Heo, Jae-Han Lee, Kyung-Rae Kim, Han-Ul Kim, Chang-Su Kim

We propose a monocular depth estimation algorithm, which extracts a depth map from a single image, based on whole strip masking (WSM) and reliability-based refinement.

Monocular Depth Estimation

Single-Image Depth Estimation Based on Fourier Domain Analysis

1 code implementation CVPR 2018 Jae-Han Lee, Minhyeok Heo, Kyung-Rae Kim, Chang-Su Kim

We propose a deep learning algorithm for single-image depth estimation based on the Fourier frequency domain analysis.

Depth Estimation

Semantic Line Detection and Its Applications

1 code implementation ICCV 2017 Jun-Tae Lee, Han-Ul Kim, Chul Lee, Chang-Su Kim

Then, we develop the line pooling layer to extract a feature vector for each candidate line from the feature maps.

Classification General Classification +4

Temporal Superpixels Based on Proximity-Weighted Patch Matching

no code implementations ICCV 2017 Se-Ho Lee, Won-Dong Jang, Chang-Su Kim

A temporal superpixel algorithm based on proximity-weighted patch matching (TS-PPM) is proposed in this work.

Patch Matching Superpixels

Online Video Object Segmentation via Convolutional Trident Network

no code implementations CVPR 2017 Won-Dong Jang, Chang-Su Kim

A semi-supervised online video object segmentation algorithm, which accepts user annotations about a target object at the first frame, is proposed in this work.

Object Optical Flow Estimation +4

Contour-Constrained Superpixels for Image and Video Processing

no code implementations CVPR 2017 Se-Ho Lee, Won-Dong Jang, Chang-Su Kim

We initialize superpixel labels in each frame by transferring those in the previous frame and refine the labels to make superpixels temporally consistent as well as compatible with object contours.

Object Superpixels

SOWP: Spatially Ordered and Weighted Patch Descriptor for Visual Tracking

no code implementations ICCV 2015 Han-Ul Kim, Dae-Youn Lee, Jae-Young Sim, Chang-Su Kim

The patch weights represent the importance of each patch in the description of foreground information, and are used to construct an object descriptor, called spatially ordered and weighted patch (SOWP) descriptor.

Object Visual Tracking

Multihypothesis Trajectory Analysis for Robust Visual Tracking

no code implementations CVPR 2015 Dae-Youn Lee, Jae-Young Sim, Chang-Su Kim

The notion of multihypothesis trajectory analysis (MTA) for robust visual tracking is proposed in this work.

Visual Tracking

Multiple Random Walkers and Their Application to Image Cosegmentation

no code implementations CVPR 2015 Chulwoo Lee, Won-Dong Jang, Jae-Young Sim, Chang-Su Kim

A graph-based system to simulate the movements and interactions of multiple random walkers (MRW) is proposed in this work.

Clustering

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