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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Unsupervised Moving Object Detection via Contextual Information Separation |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| ISBN | 978-1-7281-3293-8 |
| Page Range | 879 - 888 |
| Event Title | 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Event Type | conference |
| Event Location | Long Beach, CA, USA |
| Event Start Date | July 15 - 2019 |
| Event End Date | July 20 - 2019 |
| Publisher | IEEE |
| Abstract Text | We propose an adversarial contextual model for detecting moving objects in images. A deep neural network is trained to predict the optical flow in a region using information from everywhere else but that region (context), while another network attempts to make such context as uninformative as possible. The result is a model where hypotheses naturally compete with no need for explicit regularization or hyper-parameter tuning. Although our method requires no supervision whatsoever, it outperforms several methods that are pre-trained on large annotated datasets. Our model can be thought of as a generalization of classical variational generative region-based segmentation, but in a way that avoids explicit regularization or solution of partial differential equations at run-time. |
| Digital Object Identifier | 10.1109/cvpr.2019.00097 |
| Other Identification Number | merlin-id:20288 |
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