• Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
• • Environment
  • Datasets
  • Models && features && adjacency matrices
  • Usage
  • • Run Microsoft COCO
    • Run Pascal VOC 2007
    • Run Pascal VOC 2012
    • Run VisualGenome-500
  • Result
  • Citation
  • Contributing

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 (tianshuichen@gmail.com & xumx7@mail2.sysu.edu.cn & huixlu@mail2.sysu.edu.cn)