Semantic correspondence
70 papers with code • 5 benchmarks • 7 datasets
The task of semantic correspondence aims to establish reliable visual correspondence between different instances of the same object category.
Benchmarks
These leaderboards are used to track progress in Semantic correspondence
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CUB-200-2011
Caltech-101
SPair-71k
PF-PASCAL
PF-WILLOW
FunKPoint
AVMIT
Most implemented papers
Correspondence Networks with Adaptive Neighbourhood Consensus
ActiveVisionLab/ANCNet
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CVPR 2020
This is a challenging task due to large intra-class variations and a lack of dense pixel level annotations.
Attentive Normalization for Conditional Image Generation
shepnerd/AttenNorm
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CVPR 2020
Traditional convolution-based generative adversarial networks synthesize images based on hierarchical local operations, where long-range dependency relation is implicitly modeled with a Markov chain.
Semantic Correspondence via 2D-3D-2D Cycle
Visual semantic correspondence is an important topic in computer vision and could help machine understand objects in our daily life.
Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution
EvelynFan/FAU
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The intensity estimation of facial action units (AUs) is challenging due to subtle changes in the person's facial appearance.
Feature Robust Optimal Transport for High-dimensional Data
Mathux/FROT
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To show the effectiveness of FROT, we propose using the FROT algorithm for the layer selection problem in deep neural networks for semantic correspondence.
Semantic Correspondence as an Optimal Transport Problem
Establishing dense correspondences across semantically similar images is a challenging task.
A Sparse and Locally Coherent Morphable Face Model for Dense Semantic Correspondence Across Heterogeneous 3D Faces
The 3D Morphable Model (3DMM) is a powerful statistical tool for representing 3D face shapes.
Learning to Compose Hypercolumns for Visual Correspondence
Feature representation plays a crucial role in visual correspondence, and recent methods for image matching resort to deeply stacked convolutional layers.
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence
liuf1990/Implicit_Dense_Correspondence
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NeurIPS 2020
The goal of this paper is to learn dense 3D shape correspondence for topology-varying objects in an unsupervised manner.
CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation
microsoft/CoCosNet-v2
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CVPR 2021
We present the full-resolution correspondence learning for cross-domain images, which aids image translation.
