SCNet: Learning Semantic Correspondence

Related tags

Deep LearningSCNet
Overview

SCNet Code

Region matching code is contributed by Kai Han ([email protected]).

Dense matching code is contributed by Rafael S. Rezende ([email protected]).

This code is written in MATLAB, and implements the SCNet[1]. For the dataset, see our project page: http://www.di.ens.fr/willow/research/scnet.

Install Dependencies

Codes

SCNet_Matconvnet

Additional Matconvnet modules implemented for SCNet. These code should be copied into matconvnet/matlab/ folder.

SCNet

This is the primary net work training and testing code.

  • SCNet_A_init.m, SCNet_AG_init.m, SCNet_AGplus_init.m: initialize the SCNet_A, SCNet_AG, SCNet_AG+.

  • SCNet_A.m, SCNet_AG.m, SCNet_AGplus.m: train SCNet_A, SCNet_AG, SCNet_AG+.

  • eva_PCR_mIoU_SCNet_A.m, eva_PCR_mIoU_SCNet_AG.m, eva_PCR_mIoU_SCNet_AGplus.m: evaluate the trained nets.

  • eva_PCR_mIoU_ImageNet_SCNet_A.m, eva_PCR_mIoU_ImageNet_SCNet_AG.m, eva_PCR_mIoU_ImageNet_SCNet_AGplus.m: evaluate SCNets with ImageNet pretrained parameters, i.e., SCNets without training.

SCNet_Baselines

Comparison code for our SCNet features and HOG features with NAM, PHM and LOM in Proposal Flow [2, 3].

  • NAM_HOG_eva.m, PHM_HOG_eva.m, LOM_HOG_eva.m: evaluate NAM, PHM, and LOM with HOG features.

  • NAM_SCNet_eva.m, PHM_SCNet_eva.m, LOM_SCNet_eva.m: evaluate NAM, PHM, and LOM with learned SCNet features.

  • HOG_SCNet_AG_eva.m: replace the learned SCNet feature by HOG feature in SCNet_AG model.

Data

We used PF-PASCAL, PF-WILLOW, PASCAL Parts and CUB data sets and follows Proposal Flow[2, 3] to generate our trainging data.

Triaining data preparation code is put in PF-PASCAL-code folder.

Notes

  • The code is provided for academic use only. Use of the code in any commercial or industrial related activities is prohibited.
  • If you use our code or dataset, please cite the paper.
@InProceedings{han2017scnet,
author = {Kai Han and Rafael S. Rezende and Bumsub Ham and Kwan-Yee K. Wong and Minsu Cho and Cordelia Schmid and Jean Ponce},
title = {SCNet: Learning Semantic Correspondence},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2017}
}

References

[1] Kai Han, Rafael S. Rezende, Bumsub Ham, Kwan-Yee K. Wong, Minsu Cho, Cordelia Schmid, Jean Ponce, "SCNet: Learning Semantic Correspondence", International Conference on Computer Vision (ICCV), 2017.

[2] Bumsub Ham, Minsu Cho, Cordelia Schmid, Jean Ponce, "Proposal Flow: Semantic Correspondences from Object Proposals", IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 2017

[3] Bumsub Ham, Minsu Cho, Cordelia Schmid, Jean Ponce, "Proposal Flow", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016

Owner
Kai Han
Visual Geometry Group (VGG)
Kai Han
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