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Bottom-Up Attention Guidance for Recurrent Image Recognition

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


Original languageEnglish
Title of host publication2018 25th IEEE International Conference on Image Processing (ICIP)
Number of pages5
ISBN (Electronic)978-1-4799-7061-2
ISBN (Print)978-1-4799-7062-9
Publication statusPublished - Oct 2018
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Image Processing -
Duration: 1 Jan 1900 → …

Publication series

ISSN (Electronic)2381-8549


ConferenceIEEE International Conference on Image Processing
Period1/01/00 → …


This paper presents a recurrent neural network architecture, guided by the bottom-up attention, for the recognition task. The proposed architecture processes an input image as a sequence of selectively chosen patches. The patches are chosen from the salient regions of the input image. Using human driven saliency maps from gaze, the benefit of such a selection process is first shown. Next, the performance of computational models of bottom-up attention are assessed as alternative to human attention.


  • image recognition, recurrent neural nets, human driven saliency maps, recurrent image recognition, recurrent neural network architecture, recognition task, bottom-up attention guidance, Computational modeling, Task analysis, Computer architecture, Image recognition, Predictive models, Pipelines, Feature extraction, Recurrent neural networks, gaze, saliency, deep neural networks

Publication forum classification

Field of science, Statistics Finland