49 citations · 79 across the 8 of their papers we have counts for
8 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…
Unsupervised Scene Sketch to Photo Synthesis
Jiayun Wang, Sangryul Jeon, Stella X. Yu +3
Sketches make an intuitive and powerful visual expression as they are fast executed freehand drawings. We present a method for synthesizing realistic photos from scene sketches. Wi…
CATs: Cost Aggregation Transformers for Visual Correspondence
Seokju Cho, Sunghwan Hong, Sangryul Jeon +3
We propose a novel cost aggregation network, called Cost Aggregation Transformers (CATs), to find dense correspondences between semantically similar images with additional challeng…
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…
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…
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…