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Erschienen in:
Snap Angle Prediction for 360 Panoramas Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency

verfasst von : Eunji Chong, Nataniel Ruiz, Yongxin Wang, Yun Zhang, Agata Rozga, James M. Rehg

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

This paper addresses the challenging problem of estimating the general visual attention of people in images. Our proposed method is designed to work across multiple naturalistic social scenarios and provides a full picture of the subject’s attention and gaze. In contrast, earlier works on gaze and attention estimation have focused on constrained problems in more specific contexts. In particular, our model explicitly represents the gaze direction and handles out-of-frame gaze targets. We leverage three different datasets using a multi-task learning approach. We evaluate our method on widely used benchmarks for single-tasks such as gaze angle estimation and attention-within-an-image, as well as on the new challenging task of generalized visual attention prediction. In addition, we have created extended annotations for the MMDB and GazeFollow datasets which are used in our experiments, which we will publicly release.

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Vorheriges Kapitel Online Multi-Object Tracking with Dual Matching Attention Networks
Nächstes Kapitel Videos as Space-Time Region Graphs
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Metadaten
Titel
Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency
verfasst von
Eunji Chong
Nataniel Ruiz
Yongxin Wang
Yun Zhang
Agata Rozga
James M. Rehg
Copyright-Jahr
2018
Buch
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01227-4

Electronic ISBN: 978-3-030-01228-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01228-1_24

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