A Novel Spatio-Temporal 3D Convolutional Encoder-Decoder Network for Dynamic Saliency Prediction

As human beings are living in an always changing environment, predicting saliency maps from dynamic visual stimulus is of importance for modeling human visual system. Compared with human behavior, recent models based on LSTM and 3DCNN are still not good enough due to the limitation in spatio-tempora...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE access Vol. 9; p. 1
Main Authors:	Li, Hao, Qi, Fei, Shi, Guangming
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	3D Fully Convolutional Networks Changing environments Coders Computational modeling Correlation coefficients Decoding Dynamic Saliency Prediction Encoders-Decoders Feature extraction Predictions Predictive models Salience Solid modeling Spatio-Temporal Features Three-dimensional displays Visual Attention Visual stimuli Visualization
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!