Research

The source codes or supplemental materials for our selected papers are available. Please refer to the corresponding papers, when you use the codes or materials.

Order Learning

▶ Blind Image Quality Assessment Based on Geometric Order Learning
Nyeong-Ho Shin, Seon-Ho Lee and Chang-Su Kim, “Blind Image Quality Assessment Based on Geometric Order Learning,” CVPR, Jun. 2024. [ pdf ] [ supp ] [ code ]
▶ Unsupervised Order Learning
Seon-Ho Lee, Nyeong-Ho Shin and Chang-Su Kim, “Unsupervised Order Learning,” ICLR, May. 2024. [ pdf ]
▶ Geometric Order Learning for Rank Estimation
Seon-Ho Lee, Nyeong-Ho Shin and Chang-Su Kim, “Geometric Order Learning for Rank Estimation,” NeurIPS, Dec. 2022. [ pdf ]
▶ Order Learning Using Partially Ordered Data via Chainization
Seon-Ho Lee and Chang-Su Kim, “Order Learning Using Partially Ordered Data via Chainization,” ECCV, Oct. 2022. [ pdf ] [ code ]
▶ Moving Window Regression: A Novel Approach to Ordinal Regression
Nyeong-Ho Shin, Seon-Ho Lee and Chang-Su Kim, “Moving Window Regression: A Novel Approach to Ordinal Regression,” CVPR, Jun. 2022. [ pdf ] [ code ]
▶ Deep Repulsive Clustering of Ordered Data Based on Order-Identity Decomposition
Seon-Ho Lee and Chang-Su Kim, “Deep Repulsive Clustering of Ordered Data Based on Order-Identity Decomposition,” ICLR, May. 2021. [ pdf ] [ code ]
▶ Order Learning and Its Application to Age Estimation
Kyungsun Lim, Nyeong-Ho Shin, Young-Yoon Lee, and Chang-Su Kim, “Order Learning and Its Application to Age Estimation,” ICLR, Apr. 2020. [ pdf ] [ code ]

Face Obfuscation

▶ Forbes: Face Obfuscation Rendering via Backpropagation Refinement Scheme
Jintae Kim, Seungwon Yang, Seong-Gyun Jeong and Chang-Su Kim, “Forbes: Face Obfuscation Rendering via Backpropagation Refinement Scheme,” ECCV, Sep. 2024. [ pdf ] [ code ]

Data Compression

▶ Context-based Trit-Plane Coding for Progressive Image Compression
Seungmin Jeon, Kwang Pyo Choi, Youngo Park and Chang-Su Kim, “Context-based Trit-Plane Coding for Progressive Image Compression,” CVPR, Jun. 2023. [ pdf ] [ code ] [ supp ]
▶ DPICT: Deep Progressive Image Compression Using Trit-Planes
Jae-Han Lee, Seungmin Jeon, Kwang Pyo Choi, Youngo Park and Chang-Su Kim, “DPICT: Deep Progressive Image Compression Using Trit-Planes,” CVPR, Jun. 2022. [ pdf ] [ code ] [ supp ]

Object Description

▶ OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection
Dongkwon Jin and Chang-Su Kim, “OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection,” ECCV, Sep. 2024. [ pdf ] [ code ]
▶ Recursive Video Lane Detection
Dongkwon Jin, Dahyun Kim and Chang-Su Kim, “Recursive Video Lane Detection,” ICCV, Oct. 2023. [ pdf ] [ code ] [ supp ]
▶ Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes
Dongkwon Jin, Wonhui Park, Seong-Gyun Jeong, Heeyeon Kwon and Chang-Su Kim, “Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes,” CVPR, Jun. 2022. [ pdf ] [ code ] [ supp ]
▶ Eigencontours: Novel Contour Descriptors Based on Low-Rank
Wonhui Park, Dongkwon Jin, and Chang-Su Kim, “Eigencontours: Novel Contour Descriptors Based on Low-Rank,” CVPR, Jun. 2022. [ pdf ] [ code ]

Frame-Rate Up-Conversion

▶ BiFormer: Learning Bilateral Motion Estimation via Bilateral Transformer for 4K Video Frame Interpolation
Junhuem Park, Jintae Kim, and Chang-Su Kim, “BiFormer: Learning Bilateral Motion Estimation via Bilateral Transformer for 4K Video Frame Interpolation,” CVPR, Jun. 2023. [ pdf ] [ code ]
▶ Asymmetric Bilateral Motion Estimation for Video Frame Interpolation
Junhuem Park, Chul Lee, and Chang-Su Kim, “Asymmetric Bilateral Motion Estimation for Video Frame Interpolation,” ICCV, Oct. 2021. [ pdf ] [ code ]
▶ BMBC: Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation
Junhuem Park, Keunsoo Ko, Chul Lee, and Chang-Su Kim, “BMBC: Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation,” ECCV, Aug. 2020. [ pdf ] [ code ]
▶ Motion-compensated frame interpolation based on multihypothesis motion estimation and texture optimization
Seong-Gyun Jeong, Chul Lee, and Chang-Su Kim, “Motion-compensated frame interpolation based on multihypothesis motion estimation and texture optimization,” IEEE TIP, Nov. 2013. [ pdf ] [ code ]

Inpainting

▶ Continuously Masked Transformer for Image Inpainting
Keunsoo Ko, and Chang-Su Kim, “Continuously Masked Transformer for Image Inpainting,” ICCV, Oct. 2023.
[ pdf ] [ code ]

Super-Resolution

▶ Light Field Super-Resolution via Adaptive Feature Remixing
Keunsoo Ko, Yeong Jun Koh, Soonkeun Chang, and Chang-Su Kim, “Light Field Super-Resolution via Adaptive Feature Remixing,” IEEE TIP, Apr. 2021.
[ pdf ] [ code ]

Single-Image Depth Estimation

▶ Masked Spatial Propagation Network for Sparsity-Adaptive Depth Refinement
Jinyoung Jun, Jae-Han Lee and Chang-Su Kim, “Masked Spatial Propagation Network for Sparsity-Adaptive Depth Refinement,” CVPR, Jun. 2024. [ pdf ] [ code ]
▶ Depth Map Decomposition for Monocular Depth Estimation
Jinyoung Jun, Jae-Han Lee, Chul Lee and Chang-Su Kim, “Depth Map Decomposition for Monocular Depth Estimation,” ECCV, Oct. 2022. [ pdf ] [ code ]
▶ Multi-Loss Rebalancing Algorithm for Monocular Depth Estimation
Jae-Han Lee and Chang-Su Kim, “Multi-Loss Rebalancing Algorithm for Monocular Depth Estimation,” ECCV, Aug. 2020. [ pdf ] [ code ]
▶ Monocular Depth Estimation Using Relative Depth Maps
Jae-Han Lee and Chang-Su Kim, “Monocular Depth Estimation Using Relative Depth Maps,” CVPR, Jun. 2019.
▶ Single-Image Depth Estimation Based on Fourier Domain Analysis
Jae-Han Lee, Minhyeok Heo, Kyung-Rae Kim, and Chang-Su Kim, “Single-Image Depth Estimation Based on Fourier Domain Analysis,” CVPR, Jun. 2018.

Semantic Lines and Photographic Composition

▶ Semantic Line Combination Detector
Jinwon Ko, Dongkwon Jin and Chang-Su Kim, “Semantic Line Combination Detector,” CVPR, Jun. 2024. [ pdf ] [ code ]
▶ Harmonious Semantic Line Detection via Maximal Weight Clique Selection
Dongkwon Jin, Wonhui Park, Seong-Gyun Jeong, and Chang-Su Kim, “Harmonious Semantic Line Detection via Maximal Weight Clique Selection,” CVPR, Jun. 2021. [ pdf ] [ supp ] [ code ]
▶ Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching
Dongkwon Jin, Jun-Tae Lee, and Chang-Su Kim, “Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching,” ECCV, Aug. 2020. [ pdf ] [ supp ] [ code ]
▶ Photographic Composition Classification and Dominant Geometric Element Detection for Outdoor Scenes
Jun-Tae Lee, Han-Ul Kim, Chul Lee, and Chang-Su Kim, “Photographic Composition Classification and Dominant Geometric Element Detection for Outdoor Scenes,” JVCIR, Aug. 2018. [ pdf ][ code ][ dataset ]
▶Semantic Line Detection and Its Applications
Jun-Tae Lee, Han-Ul Kim, Chul Lee, and Chang-Su Kim, “Semantic Line Detection and Its Applications,” ICCV, Oct. 2017. [ dataset ][ code ][ code_v2 ]

Image Enhancement

▶ Global and Local Enhancement Networks For Paired and Unpaired Image Enhancement
Han-Ul Kim, Young Jun Koh, and Chang-Su Kim, “Global and Local Enhancement Networks For Paired and Unpaired Image Enhancement,” ECCV, Aug. 2020. [ pdf ] [ code ]
▶ PieNet: Personalized Image Enhancement
Han-Ul Kim, Young Jun Koh, and Chang-Su Kim, “PieNet: Personalized Image Enhancement,” ECCV, Aug. 2020. [ pdf ] [ code ]
▶ Optimized brightness compensation and contrast enhancement for transmissive liquid crystal displays
Chul Lee, Jin-Hwan Kim, Chulwoo Lee, and Chang-Su Kim, “Optimized brightness compensation and contrast enhancement for transmissive liquid crystal displays,” IEEE TCSVT, Apr. 2014. [ pdf ] [ code ]
▶ Contrast Enhancement Based on Layered Difference Representation of 2D Histograms
Chulwoo Lee, Chul Lee, and Chang-Su Kim, “Contrast enhancement based on layered difference representation of 2D histograms,” IEEE TIP, Dec. 2013.
[ pdf ] [ code ]
▶ Power-constrained contrast enhancement for emissive displays based on histogram equalization
Chulwoo Lee, Chul Lee, Young-Yoon Lee, and Chang-Su Kim, “Power-constrained contrast enhancement for emissive displays based on histogram equalization,” IEEE TIP, Jan. 2012. [ pdf ] [ code ]

Clustering, Segmentation, and Saliency

▶ MFP: Making Full Use of Probability Maps for Interactive Image Segmentation
Chaewon Lee, Seon-Ho Lee, and Chang-Su Kim, “MFP: Making Full Use of Probability Maps for Interactive Image Segmentation,” CVPR, Jun. 2024.
[ pdf ] [ supp ] [ code ]
▶ Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps
Yuk Heo, Yeong Jun Koh, and Chang-Su Kim, “Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps,” CVPR, Jun. 2021.
[ pdf ] [ supp ] [ code ] [video1-youtube] [video2-youtube]
▶ Interactive Video Object Segmentation Using Global and Local Transfer Modules
Yuk Heo, Yeong Jun Koh, and Chang-Su Kim, “Interactive Video Object Segmentation Using Global and Local Transfer Modules,” ECCV, Aug. 2020.
[ pdf ] [ code ]
▶ Temporal Superpixels Based on Proximity-Weighted Patch Matching
Se-Ho Lee, Won-Dong Jang and Chang-Su Kim, “Temporal Superpixels Based on Proximity-Weighted Patch Matching,” ICCV, Oct. 2017. [ pdf ] [ code ]
▶ Online Video Object Segmentation via Convolutional Trident Network
Won-Dong Jang, and Chang-Su Kim, “Online Video Object Segmentation via Convolutional Trident Network,” CVPR, Jul. 2017. [ pdf ] [ code ]
▶ Primary Object Segmentation in Videos Based on Region Augmentation and Reduction
Yeong Jun Koh, and Chang-Su Kim, “Primary Object Segmentation in Videos Based on Region Augmentation and Reduction,” CVPR, Jul. 2017. [ pdf ]
▶ Contour-Constrained Superpixels for Image and Video Processing
Se-Ho Lee, Won-Dong Jang and Chang-Su Kim, “Contour-Constrained Superpixels for Image and Video Processing,” CVPR, Jul. 2017. [ pdf ] [ code ]
▶ Streaming Video Segmentation via Short-Term Hierarchical Segmentation and Frame-by-Frame Markov Random Field Optimization
Won-Dong Jang, and Chang-Su Kim, “Streaming Video Segmentation via Short-Term Hierarchical Segmentation and Frame-by-Frame Markov Random Field Optimization,” ECCV, Oct. 2016. [ pdf ] [ code ]
▶ Semi-supervised Video Object Segmentation Using Multiple Random Walkers
Won-Dong Jang, and Chang-Su Kim, “Semi-supervised Video Object Segmentation Using Multiple Random Walkers,” BMVC, Sep. 2016. [ pdf ] [ code ]
▶ Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions
Won-Dong Jang, Chulwoo Lee, and Chang-Su Kim, “Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions,” CVPR, Jun. 2016. [ pdf ] [ code ] [ dataset ]
▶ Compressed Domain Video Saliency Detection Using Global and Local Spatiotemporal Features
Se-Ho Lee, Je-Won Kang, and Chang-Su Kim, “Compressed Domain Video Saliency Detection Using Global and Local Spatiotemporal Features,” JVCI, Feb. 2016. [ pdf ]
▶ Spatiotemporal Saliency Detection for Video Sequences Based on Random Walk with Restart
Hansang Kim, Youngbae Kim, Jae-Young Sim, and Chang-Su Kim, “Spatiotemporal saliency detection for video sequences based on random walk with restart,” IEEE TIP, Aug. 2015. [ pdf ] [ code ]
▶ Multiple Random Walkers and Their Application to Image Cosegmentation
Chulwoo Lee, Won-Dong Jang, Jae-Young Sim, and Chang-Su Kim, “Multiple Random Walkers and Their Application to Image Cosegmentation,” CVPR, Jun. 2015. [ pdf ] [ code ]
▶ Multiscale Saliency Detection Using Random Walk with Restart
Jun-Seong Kim, Jae-Young Sim, and Chang-Su Kim, “Multiscale saliency detection using random walk with restart,” IEEE TCSVT, Feb. 2014. [ pdf ] [ code ]

Object Tracking, Object Discovery, and Motion Estimation

▶ Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression
Kyung-Rae Kim, Whan Choi, Yeong Jun Koh, Seong-Gyun Jeong, and Chang-Su Kim, “Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression,” ICCV, Oct. 2019. [ pdf ][ supp ][ source code ][ dataset ][ dataset_animal ]
▶ Unsupervised Primary Object Discovery in Videos Based on Evolutionary Primary Object Modeling with Reliable Object Proposals
Yeong Jun Koh, and Chang-Su Kim, “Unsupervised Primary Object Discovery in Videos Based on Evolutionary Primary Object Modeling with Reliable Object Proposals,” IEEE TIP, Nov. 2017.
▶ Locator-Checker-Scaler Object Tracking Using Spatially Ordered and Weighted Patch Descriptor
Han-Ul Kim and Chang-Su Kim, “Locator-Checker-Scaler Object Tracking Using Spatially Ordered and Weighted Patch Descriptor,” IEEE TIP, Aug. 2017. [ pdf ] [ code ]
▶ CDT: Cooperative Detection and Tracking for Tracing Multiple Objects in Video Sequences
Han-Ul Kim and Chang-Su Kim, “CDT: Cooperative Detection and Tracking for Tracing Multiple Objects in Video Sequences,” ECCV, Oct. 2016. [ pdf ] [ code ]
▶ POD: Discovering Primary Objects in Videos Based on Evolutionary Refinement of Object Recurrence, Background, and Primary Object Models
Yeong Jun Koh, Won-Dong Jang, and Chang-Su Kim, “POD: Discovering Primary Objects in Videos Based on Evolutionary Refinement of Object Recurrence, Background, and Primary Object Models,” CVPR, Jun. 2016.[ pdf ] [ supp ] [ dataset ]
▶ SOWP: Spatially Ordered and Weighted Patch Descriptor for Visual Tracking
Han-Ul Kim, Dae-Youn Lee, Jae-Young Sim, and Chang-Su Kim, “SOWP: Spatially Ordered and Weighted Patch Descriptor for Visual Tracking,” ICCV, Dec. 2015. [ pdf ] [ code ]
▶ Multihypothesis Trajectory Analysis for Robust Visual Tracking
Dae-Youn Lee, Jae-Young Sim, and Chang-Su Kim, “Multihypothesis Trajectory Analysis for Robust Visual Tracking,” CVPR, Jun. 2015. [ pdf ] [ code ]
▶ Visual Tracking Using Pertinent Patch Selection and Masking
Dae-Youn Lee, Jae-Young Sim, and Chang-Su Kim, “Visual tracking using pertinent patch selection and masking,” CVPR, Jun. 2014. [ pdf ] [ code ]

Image Assessment

▶Image Aesthetic Assessment Based on Pairwise Comparison – A Unified Approach to Score Regression, Binary Classification, and Personalization
Jun-Tae Lee and Chang-Su Kim, “Image Aesthetic Assessment Based on Pairwise Comparison – A Unified Approach to Score Regression, Binary Classification, and Personalization,” ICCV, Oct. 2019. [ pdf ]
▶ FDQM: Fast Quality Metric For Depth Maps Without View Synthesis
Won-Dong Jang, Tae-Young Chung, Jae-Young Sim, and Chang-Su Kim, “FDQM: Fast Quality Metric For Depth Maps Without View Synthesis,” IEEE TCSVT, Jul. 2015. [ pdf ] [ code ]

Stereo Matching

▶ Multiscale Feature Extractors for Stereo Matching Cost Computation
Kyung-Rae Kim, Yeong Jun Koh, and Chang-Su Kim, “Multiscale Feature Extractors for Stereo Matching Cost Computation,” IEEE Access, May. 2018. [ pdf ]
▶ Consistent Stereo Matching Under Varying Radiometric Conditions
Il-Lyong Jung, Tae-Young Chung, Jae-Young Sim, and Chang-Su Kim, “Consistent stereo matching under varying radiometric conditions,” IEEE TMM, Jan. 2013. [ pdf ]

Dehazing, Deraining and Desnowing

▶ Video Deraining and Desnowing Using Temporal Correlation and Low-Rank Matrix Completion
Jin-Hwan Kim, Jae-Young Sim, and Chang-Su Kim, “Video deraining and desnowing using temporal correlation and low-rank matrix completion,” IEEE TIP, Sep. 2015. [ pdf ] [ code ]
▶ Optimized contrast enhancement for real-time image and video dehazing
Jin-Hwan Kim, Won-Dong Jang, Jae-Young Sim, and Chang-Su Kim, “Optimized contrast enhancement for real-time image and video dehazing,” JVCI, Apr. 2013. [ pdf ] [ code ]

ETC.

▶ Inter-Frame Compression of 3D Point Cloud Sequences
Ji-Su Kim, Jae-Han Lee, and Chang-Su Kim, “Inter-Frame Compression of 3D Point Cloud Sequences,” ICEIC, Jan. 2019. [ pdf ]
▶ Video Stabilization Based on Feature Trajectory Augmentation and Selection and Robust Mesh Grid Warping
Yeong Jun Koh and Chang-Su Kim, “Video Stabilization Based on Feature Trajectory Augmentation and Selection and Robust Mesh Grid Warping,” IEEE TIP, Dec. 2015. [ pdf ]