Sangryul Jeon | DeepAI

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Seungryong Kim
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∙ 10/06/2022

Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence

Existing pipelines of semantic correspondence commonly include extractin...

0 Sunghwan Hong, et al. ∙

∙ 09/06/2022

Unsupervised Scene Sketch to Photo Synthesis

Sketches make an intuitive and powerful visual expression as they are fa...

0 Jiayun Wang, et al. ∙

∙ 06/04/2021

Semantic Correspondence with Transformers

We propose a novel cost aggregation network, called Cost Aggregation wit...

0 Seokju Cho, et al. ∙

∙ 10/02/2019

Joint Learning of Semantic Alignment and Object Landmark Detection

Convolutional neural networks (CNNs) based approaches for semantic align...

9 Sangryul Jeon, et al. ∙

∙ 04/05/2019

Semantic Attribute Matching Networks

We present semantic attribute matching networks (SAM-Net) for jointly es...

0 Seungryong Kim, et al. ∙

∙ 10/29/2018

Recurrent Transformer Networks for Semantic Correspondence

We present recurrent transformer networks (RTNs) for obtaining dense cor...

4 Seungryong Kim, et al. ∙

∙ 07/09/2018

PARN: Pyramidal Affine Regression Networks for Dense Semantic Correspondence Estimation

This paper presents a deep architecture for dense semantic correspondenc...

2 Sangryul Jeon, et al. ∙

∙ 02/03/2017

FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

We present a descriptor, called fully convolutional self-similarity (FCS...

0 Seungryong Kim, et al. ∙

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