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datasets SSGRL-2.0 Sep 25, 2019
images images for presentation Sep 9, 2019
networks
utils
README.md
element_wise_layer.py SSGRL-1.0 Aug 5, 2019
ggnn.py
main.py SSGRL-2.0 Sep 25, 2019
main_coco.sh SSGRL-2.0 Sep 25, 2019
main_vg.sh
main_voc07.sh
main_voc12.sh
models.py SSGRL-1.0 Aug 5, 2019
semantic.py

README.md

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.

Pipeline

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

@article{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},
    journal={arXiv preprint arXiv:1908.07325},
    year={2019}
}

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)

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