Learning Transferrable Knowledge for Semantic Segmentation With Deep Convolutional Neural Network

Seunghoon Hong, Junhyuk Oh, Honglak Lee, Bohyung Han; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 3204-3212

Abstract


We propose a novel weakly-supervised semantic segmentation algorithm based on Deep Convolutional Neural Net- work (DCNN). Contrary to existing weakly-supervised approaches, our algorithm exploits auxiliary segmentation an- notations available for different categories to guide segmentations on images with only image-level class labels. To make segmentation knowledge transferrable across categories, we design a decoupled encoder-decoder architecture with attention model. In this architecture, the model generates spatial highlights of each category presented in images using an attention model, and subsequently per- forms binary segmentation for each highlighted region using decoder. Combining attention model, the decoder trained with segmentation annotations in different categories boosts accuracy of weakly-supervised semantic segmentation. The proposed algorithm demonstrates substantially improved performance compared to the state-of-the- art weakly-supervised techniques in PASCAL VOC 2012 dataset when our model is trained with the annotations in 60 exclusive categories in Microsoft COCO dataset.

Related Material


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[bibtex]
@InProceedings{Hong_2016_CVPR,
author = {Hong, Seunghoon and Oh, Junhyuk and Lee, Honglak and Han, Bohyung},
title = {Learning Transferrable Knowledge for Semantic Segmentation With Deep Convolutional Neural Network},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}