Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

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We propose a novel weakly-supervised semantic segmentation algorithm based on Deep Convolutional Neural Network (DCNN). Contrary to existing weakly-supervised approaches, our algorithm exploits auxiliary segmentation annotations 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 performs 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-theart weakly-supervised techniques in PASCAL VOC 2012 dataset when our model is trained with the annotations in 60 exclusive categories in Microsoft COCO dataset.
Publisher
IEEE Computer Society and the Computer Vision Foundation (CVF)
Issue Date
2016-06-26
Language
English
Citation

29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp.3204 - 3212

DOI
10.1109/CVPR.2016.349
URI
http://hdl.handle.net/10203/269639
Appears in Collection
CS-Conference Papers(학술회의논문)
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