The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
End-To-End Multi-Task Learning With Attention
Open accessAbstract
We propose a novel multi-task learning architecture, which allows learning of task-specific feature-level attention. Our design, the Multi-Task Attention Network (MTAN), consists of a single shared network containing a global feature pool, together with a soft-attention module for each task. These modules allow for learning of task-specific features from the global features, whilst simultaneously allowing for features to be shared across different tasks. The architecture can be trained end-to-end and can be built upon any feed-forward neural network, is simple to implement, and is parameter efficient. We evaluate our approach on a variety of datasets, across both image-to-image predictions and image classification tasks. We show that our architecture is state-of-the-art in multi-task learning compared to existing methods, and is also less sensitive to various weighting schemes in the multi-task loss function. Code is available at https://github.com/lorenmt/mtan.
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Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., & Finn, C. (2020) · arXiv · doi:10.48550/arxiv.2001.06782 · Cited by 755
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Crawshaw, M. (2020) · arXiv · doi:10.48550/arxiv.2009.09796 · Cited by 428
Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
Zhang, W., Zhai, G., Wei, Y., Yang, X., & Ma, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01352 · Cited by 325
Metadata
- Authors
- Liu, S., Johns, E., & Davison, A. J.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 1871-1880
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963430933
- Cited by
- 1,202
- References
- 14
- FWCI
- 42.67
- Type
- conference paper
- Language
- en
- Open access
- green
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