/GraphNet

GraphNet: Learning Image Pseudo Annotations for Weakly-Supervised Semantic Segmentation, in ACM MM 2018 (oral)

GraphNet

GraphNet: Learning Image Pseudo Annotations for Weakly-Supervised Semantic Segmentation, in ACM Multimedia 2018 (oral) [Paper]

Overview

Illustration

Implementation Details

The network architecture of DeepLabv2-VGG16 serves as our network architecture of scribble and bounding box annotations experiments.

Results

Results on the PASCAL VOC 2012 dataset

method annotation w/o CRF w/ CRF
Ours: ScrGraphNet, Initial scribble 63.3 68.2
Ours: ScrGraphNet, 1-Round scribble 64.5 68.9
Ours: BboxGraphNet, Initial bounding box 57.1 63.4
Ours: BboxGraphNet, 1-Round bounding box 61.3 65.6

VOC Segmentation

Results on the PASCAL-CONTEXT dataset

method w/o CRF w/ CRF
Ours: ScrGraphNet, Initial 33.1 39.7
Ours: ScrGraphNet, 1-Round 33.9 40.2

CONTEXT Segmentation

Citation

If you find the paper useful for your research, please cite:

@INPROCEEDINGS{Pu2018GraphNet,
    author = {Pu, Mengyang and Huang, Yaping and Guan, Qingji and Zou, Qi},
    title = {GraphNet: Learning Image Pseudo Annotations for Weakly-Supervised Semantic Segmentation},
    booktitle = {ACM Multimedia},
    year = {2018}
}