49 citations · 166 across the 22 of their papers we have counts for
22 papers
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence
Sunghwan Hong, Jisu Nam, Seokju Cho +4
Existing pipelines of semantic correspondence commonly include extracting high-level semantic features for the invariance against intra-class variations and background clutters. Th…
Contour-Aware Equipotential Learning for Semantic Segmentation
Xu Yin, Dongbo Min, Yuchi Huo +1
With increasing demands for high-quality semantic segmentation in the industry, hard-distinguishing semantic boundaries have posed a significant threat to existing solutions. Inspi…
Sequential Cross Attention Based Multi-task Learning
Sunkyung Kim, Hyesong Choi, Dongbo Min
In multi-task learning (MTL) for visual scene understanding, it is crucial to transfer useful information between multiple tasks with minimal interferences. In this paper, we propo…
PointFix: Learning to Fix Domain Bias for Robust Online Stereo Adaptation
Kwonyoung Kim, Jungin Park, Jiyoung Lee +2
Online stereo adaptation tackles the domain shift problem, caused by different environments between synthetic (training) and real (test) datasets, to promptly adapt stereo models i…
Pin the Memory: Learning to Generalize Semantic Segmentation
Jin Kim, Jiyoung Lee, Jungin Park +2
The rise of deep neural networks has led to several breakthroughs for semantic segmentation. In spite of this, a model trained on source domain often fails to work properly in new…
DIML/CVL RGB-D Dataset: 2M RGB-D Images of Natural Indoor and Outdoor Scenes
Jaehoon Cho, Dongbo Min, Youngjung Kim +1
This manual is intended to provide a detailed description of the DIML/CVL RGB-D dataset. This dataset is comprised of 2M color images and their corresponding depth maps from a grea…
Self-balanced Learning For Domain Generalization
Jin Kim, Jiyoung Lee, Jungin Park +2
Domain generalization aims to learn a prediction model on multi-domain source data such that the model can generalize to a target domain with unknown statistics. Most existing appr…
On the confidence of stereo matching in a deep-learning era: a quantitative evaluation
Matteo Poggi, Seungryong Kim, Fabio Tosi +5
Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and rectified images. Along…
Adaptive confidence thresholding for monocular depth estimation
Hyesong Choi, Hunsang Lee, Sunkyung Kim +4
Self-supervised monocular depth estimation has become an appealing solution to the lack of ground truth labels, but its reconstruction loss often produces over-smoothed results acr…
Joint Learning of Semantic Alignment and Object Landmark Detection
Sangryul Jeon, Dongbo Min, Seungryong Kim +1
Convolutional neural networks (CNNs) based approaches for semantic alignment and object landmark detection have improved their performance significantly. Current efforts for the tw…
Semantic Attribute Matching Networks
Seungryong Kim, Dongbo Min, Somi Jeong +3
We present semantic attribute matching networks (SAM-Net) for jointly establishing correspondences and transferring attributes across semantically similar images, which intelligent…
A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation
Jaehoon Cho, Dongbo Min, Youngjung Kim +1
Current self-supervised methods for monocular depth estimation are largely based on deeply nested convolutional networks that leverage stereo image pairs or monocular sequences dur…
Recurrent Transformer Networks for Semantic Correspondence
Seungryong Kim, Stephen Lin, Sangryul Jeon +2
We present recurrent transformer networks (RTNs) for obtaining dense correspondences between semantically similar images. Our networks accomplish this through an iterative process…
PARN: Pyramidal Affine Regression Networks for Dense Semantic Correspondence
Sangryul Jeon, Seungryong Kim, Dongbo Min +1
This paper presents a deep architecture for dense semantic correspondence, called pyramidal affine regression networks (PARN), that estimates locally-varying affine transformation…
DCTM: Discrete-Continuous Transformation Matching for Semantic Flow
Seungryong Kim, Dongbo Min, Stephen Lin +1
Techniques for dense semantic correspondence have provided limited ability to deal with the geometric variations that commonly exist between semantically similar images. While vari…
Fast 2-D Complex Gabor Filter with Kernel Decomposition
Suhyuk Um, Jaeyoon Kim, Dongbo Min
2-D complex Gabor filtering has found numerous applications in the fields of computer vision and image processing. Especially, in some applications, it is often needed to compute 2…
FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence
Seungryong Kim, Dongbo Min, Bumsub Ham +3
We present a descriptor, called fully convolutional self-similarity (FCSS), for dense semantic correspondence. To robustly match points among different instances within the same ob…
Deeply Aggregated Alternating Minimization for Image Restoration
Youngjung Kim, Hyungjoo Jung, Dongbo Min +1
Regularization-based image restoration has remained an active research topic in computer vision and image processing. It often leverages a guidance signal captured in different fie…
DASC: Robust Dense Descriptor for Multi-modal and Multi-spectral Correspondence Estimation
Seungryong Kim, Dongbo Min, Bumsub Ham +2
Establishing dense correspondences between multiple images is a fundamental task in many applications. However, finding a reliable correspondence in multi-modal or multi-spectral i…
Efficient Splitting-based Method for Global Image Smoothing
Youngjung Kim, Dongbo Min, Bumsub Ham +1
Edge-preserving smoothing (EPS) can be formulated as minimizing an objective function that consists of data and prior terms. This global EPS approach shows better smoothing perform…
Deep Self-Convolutional Activations Descriptor for Dense Cross-Modal Correspondence
Seungryong Kim, Dongbo Min, Stephen Lin +1
We present a novel descriptor, called deep self-convolutional activations (DeSCA), designed for establishing dense correspondences between images taken under different imaging moda…
Cross-Scale Cost Aggregation for Stereo Matching
Kang Zhang, Yuqiang Fang, Dongbo Min +3
Human beings process stereoscopic correspondence across multiple scales. However, this bio-inspiration is ignored by state-of-the-art cost aggregation methods for dense stereo corr…