Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
Implementation of the paper: "Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition" (ICCV 2019) by Tianshui Chen, Muxin Xu, Xiaolu Hui, Hefeng Wu, Liang Lin.
Environment
Python 2.7 Pytorch 0.4.1 Ubuntu 14.04 LTS
Datasets
Microsoft COCO - 80 common object categories
Pascal VOC 2007 - 20 common object categories
Pascal VOC 2012 - 20 common object categories
VisualGenome - subset of VG, covering 500 most common object categories
Models && features && adjacency matrices
You can download the data files and our best models here password: ep6u
Usage
git clone https://github.com/Mu-xsan/SSGRL.git
cd SSGRL
mkdir data (download the data needed and put here)
Run Microsoft COCO
bash main_coco.sh [GPU_id] [Remark for this experiment]
Run Pascal VOC 2007
bash main_voc07.sh [GPU_id] [Remark for this experiment]
Run Pascal VOC 2012
bash main_voc12.sh [GPU_id] [Remark for this experiment]
Run VisualGenome-500
bash main_vg.sh [GPU_id] [Remark for this experiment]
Result
Microsoft COCO:
| Method | mAP | CP | CR | CF1 | OP | OR | OF1 |
|---|---|---|---|---|---|---|---|
| SSGRL | 83.8 | 89.9 | 68.5 | 76.8 | 91.3 | 70.8 | 79.7 |
Pascal VOC 2007:
| Classes | AP(SSGRL) | AP(pre) |
|---|---|---|
| aeroplane | 99.5 | 99.7 |
| bicycle | 97.1 | 98.4 |
| bird | 97.6 | 98.0 |
| boat | 97.8 | 97.6 |
| bottle | 82.6 | 85.7 |
| bus | 94.8 | 96.2 |
| car | 96.7 | 98.2 |
| cat | 98.1 | 98.8 |
| chair | 78.0 | 82.0 |
| cow | 97.0 | 98.1 |
| diningtable | 85.6 | 89.7 |
| dog | 97.8 | 98.8 |
| horse | 98.3 | 98.7 |
| motorbike | 96.4 | 97.0 |
| person | 98.8 | 99.0 |
| pottedplant | 84.9 | 86.9 |
| sheep | 96.5 | 98.1 |
| sofa | 79.8 | 85.8 |
| train | 98.4 | 99.0 |
| tvmonitor | 92.8 | 93.7 |
| mAP | 93.4 | 95.0 |
Pascal VOC 2012:
| Classes | AP(SSGRL) | AP(pre) |
|---|---|---|
| aeroplane | 99.5 | 99.7 |
| bicycle | 95.1 | 96.1 |
| bird | 97.4 | 97.7 |
| boat | 96.4 | 96.5 |
| bottle | 85.8 | 86.9 |
| bus | 94.5 | 95.8 |
| car | 93.7 | 95.0 |
| cat | 98.9 | 98.9 |
| chair | 86.7 | 88.3 |
| cow | 96.3 | 97.6 |
| diningtable | 84.6 | 87.4 |
| dog | 98.9 | 99.1 |
| horse | 98.6 | 99.2 |
| motorbike | 96.2 | 97.3 |
| person | 98.7 | 99.0 |
| pottedplant | 82.2 | 84.8 |
| sheep | 98.2 | 98.3 |
| sofa | 84.2 | 85.8 |
| train | 98.1 | 99.2 |
| tvmonitor | 93.5 | 94.1 |
| mAP | 93.9 | 94.8 |
VisualGenome-500
| Method | mAP |
|---|---|
| SSGRL | 36.6 |
Citation
@inproceedings{chen2019learning,
title={Learning semantic-specific graph representation for multi-label image recognition},
author={Chen, Tianshui and Xu, Muxin and Hui, Xiaolu and Wu, Hefeng and Lin, Liang},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={522--531},
year={2019}
}
@article{chen2020knowledge,
title={Knowledge-guided multi-label few-shot learning for general image recognition},
author={Chen, Tianshui and Lin, Liang and Hui, Xiaolu and Chen, Riquan and Wu, Hefeng},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2022},
publisher={IEEE}
}
Contributing
For any questions, feel free to open an issue or contact us ([email protected] & [email protected] & [email protected])