On the public citation graph

Joint Recovery of Dense Correspondence and Cosegmentation in Two Images

https://doi.org/10.1109/cvpr.2016.460

0 signed · 160 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-10593-2_31openalex-snapshot

Jointly Optimizing 3D Model Fitting and Fine-Grained Classification

inferredcites →https://doi.org/10.1007/978-3-319-10599-4_17openalex-snapshot

Efficient Image and Video Co-localization with Frank-Wolfe Algorithm

inferredcites →https://doi.org/10.1007/978-3-642-15558-1_3openalex-snapshot

The Generalized PatchMatch Correspondence Algorithm

inferredcites →https://doi.org/10.1007/978-3-642-33715-4_57openalex-snapshot

Multiple View Object Cosegmentation Using Appearance and Stereo Cues

inferredcites →https://doi.org/10.1007/s11263-006-7899-4openalex-snapshot

Efficient Belief Propagation for Early Vision

inferredcites →https://doi.org/10.1007/s11263-007-0077-5openalex-snapshot

Region-Based Hierarchical Image Matching

inferredcites →https://doi.org/10.1007/s11263-009-0271-8openalex-snapshot

Unsupervised Object Discovery: A Comparison

inferredcites →https://doi.org/10.1007/s11263-011-0437-zopenalex-snapshot

Fast Approximate Energy Minimization with Label Costs

inferredcites →https://doi.org/10.1007/s11263-013-0653-9openalex-snapshot

PMBP: PatchMatch Belief Propagation for Correspondence Field Estimation

inferredcites →https://doi.org/10.1007/s11263-015-0829-6openalex-snapshot

Unsupervised Feature Learning for Dense Correspondences Across Scenes

inferredcites →https://doi.org/10.1016/j.imavis.2014.12.002openalex-snapshot

Multi-scale hybrid saliency analysis for region of interest detection in very high resolution remote sensing images

inferredcites →https://doi.org/10.1023/b:visi.0000029664.99615.94openalex-snapshot

Distinctive Image Features from Scale-Invariant Keypoints

inferredcites →https://doi.org/10.1109/34.969114openalex-snapshot

Fast approximate energy minimization via graph cuts

inferredcites →https://doi.org/10.1109/cvpr.2004.1315232openalex-snapshot

Multiscale conditional random fields for image labeling

inferredcites →https://doi.org/10.1109/cvpr.2005.177openalex-snapshot

Histograms of Oriented Gradients for Human Detection

inferredcites →https://doi.org/10.1109/cvpr.2006.251openalex-snapshot

Region-Tree Based Stereo Using Dynamic Programming Optimization

inferredcites →https://doi.org/10.1109/cvpr.2006.91openalex-snapshot

Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs

inferredcites →https://doi.org/10.1109/cvpr.2010.5539868openalex-snapshot

Discriminative clustering for image co-segmentation

inferredcites →https://doi.org/10.1109/cvpr.2010.5539897openalex-snapshot

Fast approximate energy minimization with label costs

inferredcites →https://doi.org/10.1109/cvpr.2010.5540080openalex-snapshot

iCoseg: Interactive co-segmentation with intelligent scribble guidance

inferredcites →https://doi.org/10.1109/cvpr.2011.5995530openalex-snapshot

Object cosegmentation

inferredcites →https://doi.org/10.1109/cvpr.2012.6247719openalex-snapshot

Multi-class cosegmentation

inferredcites →https://doi.org/10.1109/cvpr.2012.6247745openalex-snapshot

Unsupervised co-segmentation through region matching

inferredcites →https://doi.org/10.1109/cvpr.2012.6247842openalex-snapshot

On SIFTs and their scales

inferredcites →https://doi.org/10.1109/cvpr.2013.242openalex-snapshot

Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation

inferredcites →https://doi.org/10.1109/cvpr.2013.253openalex-snapshot

Unsupervised Joint Object Discovery and Segmentation in Internet Images

inferredcites →https://doi.org/10.1109/cvpr.2013.299openalex-snapshot

Deformable Spatial Pyramid Matching for Fast Dense Correspondences

inferredcites →https://doi.org/10.1109/cvpr.2014.13openalex-snapshot

Reconstructing PASCAL VOC

inferredcites →https://doi.org/10.1109/cvpr.2014.190openalex-snapshot

Co-localization in Real-World Images

inferredcites →https://doi.org/10.1109/cvpr.2014.209openalex-snapshot

Graph Cut Based Continuous Stereo Matching Using Locally Shared Labels

inferredcites →https://doi.org/10.1109/cvpr.2014.261openalex-snapshot

Enriching Visual Knowledge Bases via Object Discovery and Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2014.435openalex-snapshot

DAISY Filter Flow: A Generalized Discrete Approach to Dense Correspondences

inferredcites →https://doi.org/10.1109/cvpr.2015.7298723openalex-snapshot

FlowWeb: Joint image set alignment by weaving consistent, pixel-wise correspondences

inferredcites →https://doi.org/10.1109/cvpr.2015.7298724openalex-snapshot

Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals

inferredcites →https://doi.org/10.1109/cvpr.2015.7298745openalex-snapshot

Generalized Deformable Spatial Pyramid: Geometry-preserving dense correspondence estimation

inferredcites →https://doi.org/10.1109/cvpr.2015.7298955openalex-snapshot

Hierarchically-constrained optical flow

inferredcites →https://doi.org/10.1109/iccv.2005.77openalex-snapshot

Discovering objects and their location in images

inferredcites →https://doi.org/10.1109/iccv.2009.5459253openalex-snapshot

Optical flow estimation on coarse-to-fine region-trees using discrete optimization

inferredcites →https://doi.org/10.1109/iccv.2011.6126239openalex-snapshot

Distributed cosegmentation via submodular optimization on anisotropic diffusion

inferredcites →https://doi.org/10.1109/iccv.2011.6126343openalex-snapshot

Semantic contours from inverse detectors

inferredcites →https://doi.org/10.1109/iccv.2013.164openalex-snapshot

Co-segmentation by Composition

inferredcites →https://doi.org/10.1109/iccv.2013.165openalex-snapshot

Cosegmentation and Cosketch by Unsupervised Learning

inferredcites →https://doi.org/10.1109/tpami.2004.1262177openalex-snapshot

What energy functions can be minimized via graph cuts?

inferredcites →https://doi.org/10.1109/tpami.2004.60openalex-snapshot

An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision

inferredcites →https://doi.org/10.1109/tpami.2007.1031openalex-snapshot

Minimizing Nonsubmodular Functions with Graph Cuts-A Review

inferredcites →https://doi.org/10.1109/tpami.2009.176openalex-snapshot

Efficient Sequential Correspondence Selection by Cosegmentation

inferredcites →https://doi.org/10.1109/tpami.2010.147openalex-snapshot

SIFT Flow: Dense Correspondence across Scenes and Its Applications

inferredcites →https://doi.org/10.1109/tpami.2010.161openalex-snapshot

Contour Detection and Hierarchical Image Segmentation

inferredcites →https://doi.org/10.1109/tpami.2011.110openalex-snapshot

A Tensor-Based Algorithm for High-Order Graph Matching

inferredcites →https://doi.org/10.1109/tpami.2012.120openalex-snapshot

SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

inferredcites →https://doi.org/10.1109/wacv.2014.6835734openalex-snapshot

Scale-Space SIFT flow

inferredcites →https://doi.org/10.1145/1015706.1015720openalex-snapshot

"GrabCut"

inferredcites →https://doi.org/10.1145/1531326.1531330openalex-snapshot

PatchMatch

inferredcites →https://doi.org/10.1145/1964921.1964965openalex-snapshot

Non-rigid dense correspondence with applications for image enhancement

inferredcites →https://doi.org/10.1145/2010324.1964965openalex-snapshot

Non-rigid dense correspondence with applications for image enhancement

inferredcites →https://doi.org/10.5244/c.25.14openalex-snapshot

PatchMatch Stereo - Stereo Matching with Slanted Support Windows

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-030-01225-0_22openalex-snapshot

Attentive Semantic Alignment with Offset-Aware Correlation Kernels

inferred← citeshttps://doi.org/10.1007/978-3-030-01228-1_16openalex-snapshot

Joint Map and Symmetry Synchronization

inferred← citeshttps://doi.org/10.1007/978-3-030-01231-1_22openalex-snapshot

PARN: Pyramidal Affine Regression Networks for Dense Semantic Correspondence

inferred← citeshttps://doi.org/10.1007/978-3-030-11015-4_23openalex-snapshot

Object Pose Estimation from Monocular Image Using Multi-view Keypoint Correspondence

inferred← citeshttps://doi.org/10.1007/978-3-030-11015-4_32openalex-snapshot

Semi-supervised Semantic Matching

inferred← citeshttps://doi.org/10.1007/978-3-030-20893-6_22openalex-snapshot

Deep Semantic Matching with Foreground Detection and Cycle-Consistency

inferred← citeshttps://doi.org/10.1007/978-3-030-20893-6_40openalex-snapshot

Deep Object Co-segmentation

inferred← citeshttps://doi.org/10.1007/978-3-030-33676-9_32openalex-snapshot

Weakly Supervised Learning of Dense Semantic Correspondences and Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-030-58542-6_30openalex-snapshot

Human Correspondence Consensus for 3D Object Semantic Understanding

inferred← citeshttps://doi.org/10.1007/978-3-030-58555-6_21openalex-snapshot

Learning to Compose Hypercolumns for Visual Correspondence

inferred← citeshttps://doi.org/10.1007/978-3-030-60633-6_8openalex-snapshot

Confidence-Aware Adversarial Learning for Self-supervised Semantic Matching

inferred← citeshttps://doi.org/10.1007/978-3-030-88007-1_32openalex-snapshot

Improve Semantic Correspondence by Filtering the Correlation Scores in both Image Space and Hough Space

inferred← citeshttps://doi.org/10.1007/978-3-031-19781-9_16openalex-snapshot

Learning Semantic Correspondence with Sparse Annotations

inferred← citeshttps://doi.org/10.1007/978-3-031-72970-6_16openalex-snapshot

Zero-Shot Image Feature Consensus with Deep Functional Maps

inferred← citeshttps://doi.org/10.1007/978-3-032-00986-9_19openalex-snapshot

Evaluating Pose Awareness and 3D Consistency in Semantic Matching

inferred← citeshttps://doi.org/10.1007/s00371-018-1578-7openalex-snapshot

Point sets joint registration and co-segmentation

inferred← citeshttps://doi.org/10.1007/s10462-026-11615-5openalex-snapshot

Beyond image generation: visual data analysis with diffusion models—a comprehensive survey

inferred← citeshttps://doi.org/10.1007/s11042-020-08794-wopenalex-snapshot

A Multi-View features hinged siamese U-Net for image Co-segmentation

inferred← citeshttps://doi.org/10.1007/s11263-022-01602-yopenalex-snapshot

Learning Contrastive Representation for Semantic Correspondence

inferred← citeshttps://doi.org/10.1007/s11704-022-2468-8openalex-snapshot

Weakly-supervised instance co-segmentation via tensor-based salient co-peak search

inferred← citeshttps://doi.org/10.1016/j.cviu.2022.103418openalex-snapshot

Non-parametric scene parsing: Label transfer methods and datasets

inferred← citeshttps://doi.org/10.1016/j.image.2020.115813openalex-snapshot

A new challenging image dataset with simple background for evaluating and developing co-segmentation algorithms

inferred← citeshttps://doi.org/10.1016/j.imavis.2020.103973openalex-snapshot

A novel co-attention computation block for deep learning based image co-segmentation

inferred← citeshttps://doi.org/10.1016/j.patcog.2020.107297openalex-snapshot

A survey on image and video cosegmentation: Methods, challenges and analyses

inferred← citeshttps://doi.org/10.1109/3dv.2018.00085openalex-snapshot

Learning to Align Images Using Weak Geometric Supervision

inferred← citeshttps://doi.org/10.1109/3dv66043.2025.00033openalex-snapshot

FOCUS - Multi-View Foot Reconstruction from Synthetically Trained Dense Correspondences

inferred← citeshttps://doi.org/10.1109/access.2019.2960162openalex-snapshot

A Review of Recent Advances in Image Co-Segmentation Techniques

inferred← citeshttps://doi.org/10.1109/access.2020.3046845openalex-snapshot

Semi-Global Context Network for Semantic Correspondence

inferred← citeshttps://doi.org/10.1109/cvpr.2017.442openalex-snapshot

Object-Aware Dense Semantic Correspondence

inferred← citeshttps://doi.org/10.1109/cvpr.2017.73openalex-snapshot

FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

inferred← citeshttps://doi.org/10.1109/cvpr.2018.00723openalex-snapshot

End-to-End Weakly-Supervised Semantic Alignment

inferred← citeshttps://doi.org/10.1109/cvpr.2019.00238openalex-snapshot

SFNet: Learning Object-Aware Semantic Correspondence

inferred← citeshttps://doi.org/10.1109/cvpr.2019.00905openalex-snapshot

DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency Detection

inferred← citeshttps://doi.org/10.1109/cvpr.2019.01262openalex-snapshot

Semantic Attribute Matching Networks

inferred← citeshttps://doi.org/10.1109/cvpr42600.2020.00452openalex-snapshot

Semantic Correspondence as an Optimal Transport Problem

inferred← citeshttps://doi.org/10.1109/cvpr42600.2020.00629openalex-snapshot

GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences

inferred← citeshttps://doi.org/10.1109/cvpr42600.2020.01021openalex-snapshot

Correspondence Networks With Adaptive Neighbourhood Consensus

inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.00296openalex-snapshot

Convolutional Hough Matching Networks

inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.01295openalex-snapshot

PatchMatch-Based Neighborhood Consensus for Semantic Correspondence

inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.00850openalex-snapshot

TransforMatcher: Match-to-Match Attention for Semantic Correspondence

inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.00851openalex-snapshot

Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences

inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.01908openalex-snapshot

Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels

inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.00688openalex-snapshot

MISC210K: A Large-Scale Dataset for Multi-Instance Semantic Correspondence

inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.01706openalex-snapshot

Correspondence Transformers with Asymmetric Feature Learning and Matching Flow Super-Resolution

inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.00297openalex-snapshot

Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence

inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.01613openalex-snapshot

Pixel-Level Semantic Correspondence Through Layout-Aware Representation Learning and Multi-Scale Matching Integration

inferred← citeshttps://doi.org/10.1109/cvprw56347.2022.00556openalex-snapshot

Learning Co-segmentation by Segment Swapping for Retrieval and Discovery

inferred← citeshttps://doi.org/10.1109/icassp43922.2022.9747244openalex-snapshot

Joint Learning of Feature Extraction and Cost Aggregation for Semantic Correspondence

inferred← citeshttps://doi.org/10.1109/iccv.2017.192openalex-snapshot

Weakly Supervised Manifold Learning for Dense Semantic Object Correspondence

inferred← citeshttps://doi.org/10.1109/iccv.2017.203openalex-snapshot

SCNet: Learning Semantic Correspondence

inferred← citeshttps://doi.org/10.1109/iccv.2017.485openalex-snapshot

DCTM: Discrete-Continuous Transformation Matching for Semantic Flow

inferred← citeshttps://doi.org/10.1109/iccv.2017.506openalex-snapshot

Learning Dense Facial Correspondences in Unconstrained Images

inferred← citeshttps://doi.org/10.1109/iccv.2019.00210openalex-snapshot

Dynamic Context Correspondence Network for Semantic Alignment

inferred← citeshttps://doi.org/10.1109/iccv.2019.00349openalex-snapshot

Hyperpixel Flow: Semantic Correspondence With Multi-Layer Neural Features

inferred← citeshttps://doi.org/10.1109/iccv48922.2021.00334openalex-snapshot

Multi-scale Matching Networks for Semantic Correspondence

inferred← citeshttps://doi.org/10.1109/iccv48922.2021.00339openalex-snapshot

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision

inferred← citeshttps://doi.org/10.1109/iccv48922.2021.00383openalex-snapshot

End-to-end robust joint unsupervised image alignment and clustering

inferred← citeshttps://doi.org/10.1109/iccv48922.2021.00686openalex-snapshot

Hypercorrelation Squeeze for Few-Shot Segmenation

inferred← citeshttps://doi.org/10.1109/iccv48922.2021.00976openalex-snapshot

Deep Matching Prior: Test-Time Optimization for Dense Correspondence

inferred← citeshttps://doi.org/10.1109/iccv48922.2021.01018openalex-snapshot

Warp Consistency for Unsupervised Learning of Dense Correspondences

inferred← citeshttps://doi.org/10.1109/iccv51070.2023.01489openalex-snapshot

Weakly Supervised Learning of Semantic Correspondence through Cascaded Online Correspondence Refinement

inferred← citeshttps://doi.org/10.1109/iccv51701.2025.00656openalex-snapshot

Weakly-Supervised Learning of Dense Functional Correspondences

inferred← citeshttps://doi.org/10.1109/iccvw54120.2021.00088openalex-snapshot

Get better 1 pixel PCK: ladder scales correspondence flow networks for remote sensing image matching in higher resolution

inferred← citeshttps://doi.org/10.1109/icip.2017.8296433openalex-snapshot

Convolutional feature pyramid fusion via attention network

inferred← citeshttps://doi.org/10.1109/ijcnn48605.2020.9206806openalex-snapshot

Image Co-segmentation with Multi-Scale Dual-Cross Correlation Network

inferred← citeshttps://doi.org/10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00226openalex-snapshot

Image Cosegmentation via Cosaliency Guided and Spline Regression

inferred← citeshttps://doi.org/10.1109/itme60234.2023.00074openalex-snapshot

Semantic Correspondence with Peripheral Position Coding

inferred← citeshttps://doi.org/10.1109/tcsvt.2023.3288370openalex-snapshot

Learning Semantic Alignment Using Global Features and Multi-Scale Confidence

inferred← citeshttps://doi.org/10.1109/thms.2020.2965429openalex-snapshot

An Ego-Vision System for Discovering Human Joint Attention

inferred← citeshttps://doi.org/10.1109/tip.2018.2842207openalex-snapshot

Mutual Learning Between Saliency and Similarity: Image Cosegmentation via Tree Structured Sparsity and Tree Graph Matching

inferred← citeshttps://doi.org/10.1109/tip.2019.2957937openalex-snapshot

Superpixel Embedding Network

inferred← citeshttps://doi.org/10.1109/tip.2021.3077138openalex-snapshot

Consistency Graph Modeling for Semantic Correspondence

inferred← citeshttps://doi.org/10.1109/tmm.2017.2739919openalex-snapshot

Seeds-Based Part Segmentation by Seeds Propagation and Region Convexity Decomposition

inferred← citeshttps://doi.org/10.1109/tpami.2017.2724510openalex-snapshot

Proposal Flow: Semantic Correspondences from Object Proposals

inferred← citeshttps://doi.org/10.1109/tpami.2017.2766072openalex-snapshot

Continuous 3D Label Stereo Matching Using Local Expansion Moves

inferred← citeshttps://doi.org/10.1109/tpami.2017.2777486openalex-snapshot

Learning Kinematic Structure Correspondences Using Multi-Order Similarities

inferred← citeshttps://doi.org/10.1109/tpami.2018.2803169openalex-snapshot

FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

inferred← citeshttps://doi.org/10.1109/tpami.2018.2865351openalex-snapshot

Convolutional Neural Network Architecture for Geometric Matching

inferred← citeshttps://doi.org/10.1109/tpami.2018.2878240openalex-snapshot

Discrete-Continuous Transformation Matching for Dense Semantic Correspondence

inferred← citeshttps://doi.org/10.1109/tpami.2020.2985395openalex-snapshot

Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-Segmentation

inferred← citeshttps://doi.org/10.1109/tpami.2020.3013620openalex-snapshot

Learning Semantic Correspondence Exploiting an Object-Level Prior

inferred← citeshttps://doi.org/10.1109/tpami.2020.3016711openalex-snapshot

NCNet: Neighbourhood Consensus Networks for Estimating Image Correspondences

inferred← citeshttps://doi.org/10.1109/tpami.2021.3123679openalex-snapshot

Pyramidal Semantic Correspondence Networks

inferred← citeshttps://doi.org/10.1109/tpami.2022.3218727openalex-snapshot

CATs++: Boosting Cost Aggregation With Convolutions and Transformers

inferred← citeshttps://doi.org/10.1109/tpami.2022.3233884openalex-snapshot

Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondence

inferred← citeshttps://doi.org/10.1109/tpami.2025.3640429openalex-snapshot

Semantic Correspondence: Unified Benchmarking and a Strong Baseline

inferred← citeshttps://doi.org/10.1109/wacv.2019.00118openalex-snapshot

Semantic Matching by Weakly Supervised 2D Point Set Registration

inferred← citeshttps://doi.org/10.1109/wacv.2019.00218openalex-snapshot

Alignment by Composition

inferred← citeshttps://doi.org/10.1109/wacv57701.2024.00214openalex-snapshot

Self-supervised Learning of Semantic Correspondence Using Web Videos

inferred← citeshttps://doi.org/10.1109/wacv61042.2026.00451openalex-snapshot

Eye-for-an-eye: Appearance Transfer with Dense Semantic Correspondence in Diffusion Models

inferred← citeshttps://doi.org/10.1145/3240508.3240650openalex-snapshot

iSPA-Net

inferred← citeshttps://doi.org/10.3929/ethz-b-000455564openalex-snapshot

GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network

inferred← citeshttps://doi.org/10.48550/arxiv.1702.00926openalex-snapshot

FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

inferred← citeshttps://doi.org/10.48550/arxiv.1808.01134openalex-snapshot

iSPA-Net: Iterative Semantic Pose Alignment Network

inferred← citeshttps://doi.org/10.48550/arxiv.1810.12155openalex-snapshot

Recurrent Transformer Networks for Semantic Correspondence

inferred← citeshttps://doi.org/10.48550/arxiv.1901.08339openalex-snapshot

Semi-Supervised Semantic Matching

inferred← citeshttps://doi.org/10.48550/arxiv.1901.08341openalex-snapshot

Semantic Matching by Weakly Supervised 2D Point Set Registration

inferred← citeshttps://doi.org/10.48550/arxiv.1906.06978openalex-snapshot

Multi-Scale Convolutions for Learning Context Aware Feature Representations

inferred← citeshttps://doi.org/10.48550/arxiv.1908.10543openalex-snapshot

SPair-71k: A Large-scale Benchmark for Semantic Correspondence

inferred← citeshttps://doi.org/10.48550/arxiv.2003.12059openalex-snapshot

Correspondence Networks with Adaptive Neighbourhood Consensus

inferred← citeshttps://doi.org/10.48550/arxiv.2007.10587openalex-snapshot

Learning to Compose Hypercolumns for Visual Correspondence

inferred← citeshttps://doi.org/10.48550/arxiv.2106.02520openalex-snapshot

CATs: Cost Aggregation Transformers for Visual Correspondence

inferred← citeshttps://doi.org/10.70675/0bd94776z371ez41e3z9afeze9c993ed9ab8openalex-snapshot

New methods for image classification, image retrieval and semantic correspondence

inferred← citeshttps://doi.org/10.70675/c91316a8ze3b7z4773z9d18z5fc27e34a150openalex-snapshot

Deep Learning for Near-duplicated Patterns Discovery and Alignment in Artworks

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.