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https://github.com/XiSHEN0220/SSR

(NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductive few-shot classification"
https://github.com/XiSHEN0220/SSR

image-retrieval meta-learning re-ranking similarity-graph transductive-few-shot-learning

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(NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductive few-shot classification"

Host: GitHub
URL: https://github.com/XiSHEN0220/SSR
Owner: XiSHEN0220
License: mit
Created: 2021-08-11T07:49:26.000Z (almost 3 years ago)
Default Branch: main
Last Pushed: 2021-11-21T04:20:03.000Z (over 2 years ago)
Last Synced: 2024-01-28T23:41:32.337Z (6 months ago)
Topics: image-retrieval, meta-learning, re-ranking, similarity-graph, transductive-few-shot-learning
Language: Python
Homepage:
Size: 3.8 MB
Stars: 31
Watchers: 5
Forks: 4
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE

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awesome-stars - XiSHEN0220/SSR - (NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductive few-shot classification" (Python)

README

        # SSR

(NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductivefew-shot classification"

[[Paper](https://papers.nips.cc/paper/2021/file/d9fc0cdb67638d50f411432d0d41d0ba-Paper.pdf)] [[Project webpage](http://imagine.enpc.fr/~shenx/SSR/)] [[Video]](http://imagine.enpc.fr/~shenx/SSR/ssr.mp4) [[Slide]](http://imagine.enpc.fr/~shenx/SSR/prez.pptx)







The project is an extension work to [SIB](https://github.com/hushell/sib_meta_learn). If our project is helpful for your research, please consider citing : 

```

@inproceedings{shen2021reranking,

  title={Re-ranking for image retrieval and transductive few-shot classification},

  author={Shen, Xi and Xiao, Yang and Hu, Shell Xu, and Sbai, Othman and Aubry, Mathieu},

  booktitle={Conference on Neural Information Processing Systems (NeurIPS)},

  year={2021}

}

```

## Table of Content

* [1. Installation](#1-installation)

* [2. Method](#2-methods-and-results)

    * [2.1 Image retrieval](#21-image-retrieval)

      * [2.1.1 SSR module](#211-ssr-module)

      * [2.1.2 Results](#212-results)    

    * [2.2 Transductive few-shot classification ](#22-transductive-few-shot-classification)

      * [2.2.1 SSR module](#221-ssr-module)

      * [2.2.2 Results](#222-results)

* [3. Acknowledgement](#3-acknowledgement)

* [4. ChangeLog](#4-changeLog)

* [5. License](#5-license)

## 1. Installation

Code is tested under **Pytorch > 1.0 + Python 3.6** environment.

Please refer to [image retrieval](https://github.com/XiSHEN0220/SSR/tree/main/image_retrieval) and [transductive few-shot classification](https://github.com/XiSHEN0220/SSR/tree/main/transductive_few_shot) to download datasets. 

## 2. Methods and Results

SSR learns updates for a similarity graph.

It decomposes the **N * N** similarity graph into **N** subgraphs 

where rows and columns of the matrix are **ordered** depending on similarities to the **subgraph reference image**.

The output of SSR is an improved similarity matrix.







### 2.1 Image retrieval 

#### 2.1.1 SSR module

Rows : the subgraph reference image (**red**) and 

the query image (**green**);

Columns : top retrieved images of the query image (**green**).

These images are ordered according to the reference image (**red**).







#### 2.1.2 Results 

To reproduce the results on image retrieval datasets (rOxford5k, rParis6k), please refer to [Image Retrieval](https://github.com/XiSHEN0220/SSR/tree/main/image_retrieval)







### 2.2 Transductive few-shot classification 

#### 2.2.1 SSR module

We illustrate our idea with an 1-shot-2way example: 

Rows: the subgraph reference image (**red**) and the support set **S**;

Columns: the support set **S** and the query set **Q**. Both **S** and **Q** are ordered according to the reference image (**red**). 







#### 2.2.2 Results

To reproduce the results on few-shot datasets (CIFAR-FS, Mini-ImageNet, TieredImageNet), please refer to [transductive few-shot classification](https://github.com/XiSHEN0220/SSR/tree/main/transductive_few_shot)







## 3. Acknowledgement

* The implementation of k-reciprocal is adapted from its [public code](https://github.com/zhunzhong07/person-re-ranking/tree/master/python-version)

* The implementation of few-shot training, evaluation and synthetic gradient is adapted from [SIB](https://github.com/hushell/sib_meta_learn)

## 4. ChangeLog

* **21/10/29**, model, evaluation + training released

## 5. License

This code is distributed under an [MIT LICENSE](LICENSE).

Note that our code depends on Pytorch, and uses datasets which each have their own respective licenses that must also be followed.