dc.contributor.author | Lu, Yao | |
dc.contributor.author | Yang, Zhirong | |
dc.contributor.author | Kannala, Juho | |
dc.contributor.author | Kaski, Samuel | |
dc.date.accessioned | 2020-08-26T09:15:41Z | |
dc.date.available | 2020-08-26T09:15:41Z | |
dc.date.created | 2019-11-19T10:04:31Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 978-1-7281-1985-4 | |
dc.identifier.uri | https://hdl.handle.net/11250/2674320 | |
dc.description.abstract | Inferring the relations between two images is an important class of tasks in computer vision. Examples of such tasks include computing optical flow and stereo disparity. We treat the relation inference tasks as a machine learning problem and tackle it with neural networks. A key to the problem is learning a representation of relations. We propose a new neural network module, contrast association unit (CAU), which explicitly models the relations between two sets of input variables. Due to the non-negativity of the weights in CAU, we adopt a multiplicative update algorithm for learning these weights. Experiments show that neural networks with CAUs are more effective in learning five fundamental image transformations than conventional neural networks. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.ispartof | 2019 International Joint Conference on Neural Networks (IJCNN) | |
dc.relation.uri | https://doi.org/10.1109/IJCNN.2019.8852344 | |
dc.title | Learning Image Relations with Contrast Association Networks | en_US |
dc.type | Chapter | en_US |
dc.description.version | acceptedVersion | en_US |
dc.identifier.doi | 10.1109/IJCNN.2019.8852344 | |
dc.identifier.cristin | 1749195 | |
dc.description.localcode | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.fulltext | preprint | |
cristin.qualitycode | 1 | |