SHIsue / ABIR

Codes of ICMR 2019 short paper "Weakly Supervised Image Retrieval via Coarse-scale Feature Fusion and Multi-level Attention Blocks"

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Weakly Supervised Image Retrieval via Coarse-scale Feature Fusion and Multi-level Attention Blocks

Xinyao Nie, Hong Lu, Zijian Wang, Jingyuan Liu, Zehua Guo

Fudan University

This repository is the code for Weakly Supervised Image Retrieval via Coarse-scale Feature Fusion and Multi-level Attention Blocks in ICMR 2019. If you cannot open the link, you can just download the paper which is uploaded in pdf format.

Requirements

Python 3, PyTorch >= 0.4.0, and make sure you have installed TensorboardX:

pip install tensorboardX

Quick Start

1. Prepare the Dataset

Our work utilized four datasets: In-shop Clothes Retrieval, CUB-200-2011, Stanford Online Products and Cars-196. In this repo, we just focus on CUB-200-2011 dataset. You can use "get_cub_train_file.py" to generate "train.txt" and "test.txt" OR download "train.txt" and "test.txt" directly.

2. Repo Structure

  ABIR
      |———— data/                                 # store CUB-200-2011 dataset
          |———— cub/
                  |———— images/
                  |———— images.txt                # you can use image.txt, image_class_labels.txt 
                  |———— image_class_labels.txt    # and train_test_split.txt to 
                  |———— train_test_split.txt      # generate train.txt and test.txt
                  |———— train.txt
                  |———— test.txt
      |———— code/                               
          |———— models/              
                  |———— VGG16_V5.py
          |———— __init__.py
          |———— config.py                               
          |———— Model2Feature.py 
          |———— train.py  
          |———— test.py  
          |———— trainer.py  

 

3. Train the Model

Run "train.py".

Results

We conduct the experiments on all commonly adopted image retrieval task datasets and utilize Recall@K metric for evaluation.

The following table shows the results on the In-Shop Clothes Retrieval dataset. Best results are marked in bold.

R@	1	10	20	30	40	50
FashionNet+Joints	41.0	64.0	68.0	71.0	73.0	73.5
FashionNet+Poselets	42.0	65.0	70.0	72.0	72.0	75.0
FashionNet	53.0	73.0	76.0	77.0	79.0	80.0
HDC	62.1	84.9	89.0	91.2	92.3	93.1
HTL	80.9	94.3	95.8	97.2	97.4	97.8
A-BIER	83.1	95.1	96.9	97.5	97.8	98.0
ABE-8	87.3	96.7	97.9	98.2	98.5	98.7
-------------	------------	------------	-----------	-----------	-----------	-----------
Our Baseline	85.4	96.1	97.3	97.8	98.1	98.3
ABIR w/o SE-block	88.1	96.9	97.6	98.1	98.3	98.5
ABIR with SE-block	89.0	97.1	98.0	98.4	98.6	98.8

The following table shows the results on the CUB-200-2011 dataset. Best results are marked in bold.

R@	1	10	20	30	40	50
margin	63.9	75.3	84.4	90.6	94.8	-
HDC	60.7	72.4	81.9	89.2	93.7	96.8
HTL	57.1	68.8	78.7	86.5	92.5	95.5
A-BIER	65.5	75.8	83.9	90.2	94.2	97.1
ABE-8	60.6	71.5	79.8	87.4	-	-
-------------	------------	------------	-----------	-----------	-----------	-----------
Our Baseline	73.1	81.9	87.6	91.4	93.8	96.2
ABIR w/o SE-block	77.5	84.1	88.7	91.7	94.2	96.3
ABIR with SE-block	78.1	84.6	88.7	91.8	94.4	96.6

The following table shows the results on the Stanford Online Products dataset. Best results are marked in bold.

R@	1	10	100	1000
Contrastive	42.0	58.2	73.8	89.1
Triplet	42.1	63.5	82.5	94.8
LiftedStruct	62.1	79.8	91.3	97.4
HDC	69.5	84.4	92.8	97.7
HTL	74.8	88.3	94.8	98.4
A-BIER	74.2	86.9	94.0	97.8
ABE-8	76.3	88.4	94.8	98.2
-------------	------------	------------	-----------	-----------
Our Baseline	71.2	85.6	93.5	97.7
ABIR w/o SE-block	74.3	87.4	94.6	98.3
ABIR with SE-block	74.8	87.7	95.0	98.5

The following table shows the results on the Cars-196 dataset. Best results are marked in bold.

R@	1	2	4	8	16	32
margin	86.9	92.7	95.6	97.6	98.7	-
HDC	83.8	89.8	93.6	96.2	97.8	98.9
HTL	81.4	88.0	92.7	95.7	97.4	99.0
A-BIER	82.0	89.0	93.2	96.1	-	-
ABE-8	85.2	90.5	94.0	96.1	-	-
-------------	------------	------------	-----------	-----------	-----------	-----------
Our Baseline	82.6	88.1	92.4	95.3	97.4	98.4
ABIR w/o SE-block	89.1	93.1	95.4	97.2	98.3	99.1
ABIR with SE-block	89.4	93.3	95.6	97.1	98.2	99.0

Citation

If this code helps your research, please cite our paper:

@inproceedings{nie2019weakly,
  title={Weakly Supervised Image Retrieval via Coarse-scale Feature Fusion and Multi-level Attention Blocks},
  author={Nie, Xinyao and Lu, Hong and Wang, Zijian and Liu, Jingyuan and Guo, Zehua},
  booktitle={Proceedings of the 2019 on International Conference on Multimedia Retrieval},
  pages={48--52},
  year={2019},
  organization={ACM}
}