Learning Image Relations with Contrast Association Networks

Yao Lu, Zhirong Yang, Juho Kannala, Samuel Kaski

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherIEEE
Number of pages7
ISBN (Electronic)9781728119854
DOIs
Publication statusPublished - 1 Jul 2019
MoE publication typeA4 Article in a conference publication
EventInternational Joint Conference on Neural Networks - Budapest, Hungary
Duration: 14 Jul 201919 Jul 2019

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July

Conference

ConferenceInternational Joint Conference on Neural Networks
Abbreviated titleIJCNN
CountryHungary
CityBudapest
Period14/07/201919/07/2019

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