The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network
Open accessAbstract
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.
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Cites
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Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
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Deep visual domain adaptation: A survey
Wang, M., & Deng, W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.083 · Cited by 2,209
Neuroscience-Inspired Artificial Intelligence
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The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes
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Fully Convolutional Instance-Aware Semantic Segmentation
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Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation
Rahman, M. A., & Wang, Y. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-50835-1_22 · Cited by 910
Metadata
- Authors
- Hong, S., Oh, J., Lee, H., & Han, B.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 3204-3212
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2257483379
- Cited by
- 170
- References
- 7
- FWCI
- 11.39
- Type
- conference paper
- Language
- en
- Open access
- green
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- Hong, S., Oh, J., Lee, H., & Han, B. (2016). Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.349
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