Patch Matching

14 papers with code • 2 benchmarks • 4 datasets

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Greatest papers with code

Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

tensorflow/models ECCV 2020

We view this work as a notable step towards building a simple procedure to harness unlabeled video sequences and extra images to surpass state-of-the-art performance on core computer vision tasks.

Optical Flow Estimation Panoptic Segmentation +2

Continuous 3D Label Stereo Matching using Local Expansion Moves

t-taniai/LocalExpStereo 28 Mar 2016

The local expansion moves extend traditional expansion moves by two ways: localization and spatial propagation.

Patch Matching Stereo Matching +1

SOLAR: Second-Order Loss and Attention for Image Retrieval

tonyngjichun/SOLAR ECCV 2020

One is focused on second-order spatial information to increase the performance of image descriptors, both local and global.

Image Retrieval Patch Matching

L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space

yuruntian/L2-Net CVPR 2017

In this paper, we propose to learn high per- formance descriptor in Euclidean space via the Convolu- tional Neural Network (CNN).

Patch Matching

Semi-Supervised Learning for Face Sketch Synthesis in the Wild

chaofengc/Face-Sketch-Wild 12 Dec 2018

Instead of supervising the network with ground truth sketches, we first perform patch matching in feature space between the input photo and photos in a small reference set of photo-sketch pairs.

Face Sketch Synthesis Patch Matching

A Large Dataset for Improving Patch Matching

rmitra/PS-Dataset 4 Jan 2018

Similarly on the Strecha dataset, we see an improvement of 3-5% for the matching task in non-planar scenes.

Patch Matching

Rotation equivariant vector field networks


In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image.

Breast Tumour Classification Colorectal Gland Segmentation: +4

HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss

yuruntian/HyNet NeurIPS 2020

Recent works show that local descriptor learning benefits from the use of L2 normalisation, however, an in-depth analysis of this effect lacks in the literature.

3D Reconstruction Patch Matching

Person Re-Identification via Recurrent Feature Aggregation

daodaofr/caffe-re-id 23 Jan 2017

We address the person re-identification problem by effectively exploiting a globally discriminative feature representation from a sequence of tracked human regions/patches.

Patch Matching Person Re-Identification