Coder Social home page Coder Social logo

sunyue11 / opam_tip2018 Goto Github PK

View Code? Open in Web Editor NEW

This project forked from pku-icst-mipl/opam_tip2018

0.0 0.0 0.0 15.65 MB

Source code of our TIP 2018 paper "Object-Part Attention Model for Fine-grained Image Classification"

Makefile 0.27% C 0.32% C++ 33.16% MATLAB 1.55% Shell 0.36% M 0.01% CMake 1.20% HTML 0.08% CSS 0.10% Jupyter Notebook 56.67% Python 3.97% Cuda 2.31%

opam_tip2018's Introduction

Introduction

This is the source code of our TIP 2018 paper "Object-Part Attention Model for Fine-grained Image Classification", Please cite the following paper if you use our code.

Yuxin Peng, Xiangteng He, and Junjie Zhao, "Object-Part Attention Model for Fine-grained Image Classification", IEEE Transactions on Image Processing (TIP), Vol. 27, No. 3, pp. 1487-1500, Mar. 2018.【pdf】

Preparation

caffe: run make in ./caffe/caffe-master Download the images and patches that we used from the link and unzipped to ./ folder.

Usage

- cd to ./CAM-master-car and execute run_demo.sh
- cd to ./ and execute train_patch.sh
- cd to ./Part_Detect/SelectiveSearch, execute run_ss.sh and run_filter_out.sh
- cd to ./Part_Detect, execute extract_param.sh, detect_part.m, extract_feature.sh and part_detector_test.m
- cd to ./, execute train_bbox.sh and train_part.sh
- select the best models for patch, bbox and part and replace them in the file extract_feature.sh, execute extract_feature.sh
- execute score_fusion.m to generate the final accuracy

Our Related Work

If you are interested in fine-grained image classification, you can check our recently published papers about it:

Xiangteng He and Yuxin Peng, "Visual-textual Attention Driven Fine-grained Representation Learning", 2017.【arXiv】

Xiangteng He, Yuxin Peng and Junjie Zhao, "Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN", 25th ACM Multimedia Conference (ACM MM), pp. 627-635, Mountain View, CA, USA, Oct. 23-27, 2017.【pdf】

Xiangteng He and Yuxin Peng, "Fine-grained Image Classification via Combining Vision and Language", 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5994-6002, Honolulu, Hawaii, USA, Jul. 21-26, 2017.【pdf】

Xiangteng He and Yuxin Peng, "Weakly Supervised Learning of Part Selection Model with Spatial Constraints for Fine-grained Image Classification", 31th AAAI Conference on Artificial Intelligence (AAAI), pp. 4075-4081, San Francisco, California, USA, Feb. 4–9, 2017.【pdf】

Tianjun Xiao, Yichong Xu, Kuiyuan Yang, Jiaxing Zhang, Yuxin Peng, and Zheng Zhang, "The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification", 28th IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 842-850, Boston, MA, USA, Jun. 7-12, 2015.【pdf】

Welcome to our Laboratory Homepage for more information about our papers, source codes, and datasets.

opam_tip2018's People

Contributors

pku-icst-mipl avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.