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FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence
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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/10.1007/978-3-319-46466-4_28openalex-snapshot
LIFT: Learned Invariant Feature Transform
inferredcites →https://doi.org/10.1007/978-3-319-46484-8_41openalex-snapshot
Deep Self-correlation Descriptor for Dense Cross-Modal Correspondence
inferredcites →https://doi.org/10.1007/978-3-642-33783-3_44openalex-snapshot
A Naturalistic Open Source Movie for Optical Flow Evaluation
inferredcites →https://doi.org/10.1007/s11263-009-0275-4openalex-snapshot
The Pascal Visual Object Classes (VOC) Challenge
inferredcites →https://doi.org/10.1023/a:1014573219977openalex-snapshot
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
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/cvpr.2005.177openalex-snapshot
Histograms of Oriented Gradients for Human Detection
inferredcites →https://doi.org/10.1109/cvpr.2007.383198openalex-snapshot
Matching Local Self-Similarities across Images and Videos
inferredcites →https://doi.org/10.1109/cvpr.2010.5539939openalex-snapshot
Secrets of optical flow estimation and their principles
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.2014.254openalex-snapshot
Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts
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.2015.7298642openalex-snapshot
Hypercolumns for object segmentation and fine-grained localization
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.2015.7298822openalex-snapshot
DASC: Dense adaptive self-correlation descriptor for multi-modal and multi-spectral correspondence
inferredcites →https://doi.org/10.1109/cvpr.2015.7298948openalex-snapshot
MatchNet: Unifying feature and metric learning for patch-based matching
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot
Fully convolutional networks for semantic segmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7299064openalex-snapshot
Learning to compare image patches via convolutional neural networks
inferredcites →https://doi.org/10.1109/cvpr.2015.7299145openalex-snapshot
Domain-size pooling in local descriptors: DSP-SIFT
inferredcites →https://doi.org/10.1109/cvpr.2016.133openalex-snapshot
Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2016.20openalex-snapshot
Learning Dense Correspondence via 3D-Guided Cycle Consistency
inferredcites →https://doi.org/10.1109/cvpr.2016.460openalex-snapshot
Joint Recovery of Dense Correspondence and Cosegmentation in Two Images
inferredcites →https://doi.org/10.1109/iccv.2011.6126343openalex-snapshot
Semantic contours from inverse detectors
inferredcites →https://doi.org/10.1109/iccv.2015.22openalex-snapshot
Discriminative Learning of Deep Convolutional Feature Point Descriptors
inferredcites →https://doi.org/10.1109/iccv.2015.458openalex-snapshot
Dense Semantic Correspondence Where Every Pixel is a Classifier
inferredcites →https://doi.org/10.1109/iccvw.2009.5457691openalex-snapshot
Efficient retrieval of deformable shape classes using local self-similarities
inferredcites →https://doi.org/10.1109/icip.2010.5654017openalex-snapshot
K-nearest neighbor search: Fast GPU-based implementations and application to high-dimensional feature matching
inferredcites →https://doi.org/10.1109/lsp.2014.2304073openalex-snapshot
A Robust SIFT Descriptor for Multispectral Images
inferredcites →https://doi.org/10.1109/tpami.2006.79openalex-snapshot
One-shot learning of object categories
inferredcites →https://doi.org/10.1109/tpami.2009.77openalex-snapshot
DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo
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.2011.131openalex-snapshot
Nonparametric Scene Parsing via Label Transfer
inferredcites →https://doi.org/10.1109/tpami.2011.222openalex-snapshot
BRIEF: Computing a Local Binary Descriptor Very Fast
inferredcites →https://doi.org/10.1109/tpami.2015.2389824openalex-snapshot
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
inferredcites →https://doi.org/10.1109/wacv.2014.6836101openalex-snapshot
Beyond PASCAL: A benchmark for 3D object detection in the wild
inferredcites →https://doi.org/10.1145/1964921.1964965openalex-snapshot
Non-rigid dense correspondence with applications for image enhancement
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.48550/arxiv.1310.1531openalex-snapshot
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
inferredcites →https://doi.org/10.48550/arxiv.1405.5769openalex-snapshot
Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1411.1091openalex-snapshot
Do Convnets Learn Correspondence?
inferredcites →https://doi.org/10.48550/arxiv.1510.05970openalex-snapshot
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
inferredcites →https://doi.org/10.48550/arxiv.1606.03558openalex-snapshot
Universal Correspondence Network
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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CoordGAN: Self-Supervised Dense Correspondences Emerge from GANs
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Virtual Correspondence: Humans as a Cue for Extreme-View Geometry
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Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style
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Joint Learning of Feature Extraction and Cost Aggregation for Semantic Correspondence
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SCNet: Learning Semantic Correspondence
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DCTM: Discrete-Continuous Transformation Matching for Semantic Flow
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FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence
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Discrete-Continuous Transformation Matching for Dense Semantic Correspondence
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Correspondence Networks with Adaptive Neighbourhood Consensus
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Semantic Correspondence via 2D-3D-2D Cycle
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Dense Contrastive Learning for Self-Supervised Visual Pre-Training
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Non-Parametric Neural Style Transfer
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Semantic Description of Objects in Images Based on Prototype Theory
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New methods for image classification, image retrieval and semantic correspondence
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Learning to localize goal-oriented actions with weak supervision
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Robust image representation for classification, retrieval and object discovery
inferred← citeshttps://doi.org/10.70675/c91316a8ze3b7z4773z9d18z5fc27e34a150openalex-snapshot
Deep Learning for Near-duplicated Patterns Discovery and Alignment in Artworks
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