Bottom-Up Attention Guidance For Recurrent Image Recognition

Keywords

Deep neural networks; Gaze; Image recognition; Recurrent neural networks; Saliency

Abstract

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.

Publication Date

8-29-2018

Publication Title

Proceedings - International Conference on Image Processing, ICIP

Number of Pages

3004-3008

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICIP.2018.8451537

Socpus ID

85062916136 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85062916136

This document is currently not available here.

Share

COinS