Abstract / Description of output
With increasing demand for efficient image and video analysis, test-time cost of scene parsing becomes critical for many large-scale or time-sensitive vision applications. We propose a dynamic hierarchical model for anytime scene labeling that allows us to achieve flexible trade-offs between efficiency and accuracy in pixel-level prediction. In particular, our approach incorporates the cost of feature computation and model inference, and optimizes the model performance for any given test-time budget by learning a sequence of image-adaptive hierarchical models. We formulate this anytime representation learning as a Markov Decision Process with a discrete-continuous state-action space. A high-quality policy of feature and model selection is learned based on an approximate policy iteration method with action proposal mechanism. We demonstrate the advantages of our dynamic non-myopic anytime scene parsing on three semantic segmentation datasets, which achieves 90% of the state-of-the-art performances by using 15% of their overall costs.
Original language | English |
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Title of host publication | Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VI |
Publisher | Springer |
Pages | 650-666 |
Number of pages | 17 |
ISBN (Electronic) | 978-3-319-46466-4 |
ISBN (Print) | 978-3-319-46465-7 |
DOIs | |
Publication status | Published - 17 Sept 2016 |
Externally published | Yes |
Event | 14th European Conference on Computer Vision 2016 - Amsterdam, Netherlands Duration: 8 Oct 2016 → 16 Oct 2016 http://www.eccv2016.org/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer, Cham |
Volume | 9910 |
ISSN (Print) | 0302-9743 |
Conference
Conference | 14th European Conference on Computer Vision 2016 |
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Abbreviated title | ECCV 2016 |
Country/Territory | Netherlands |
City | Amsterdam |
Period | 8/10/16 → 16/10/16 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- Leaf Node
- Hierarchical Model
- Markov Decision Process
- Feature Selection Method
- Reward Function