Video Saliency Forecasting Transformer-Reference-Cited by-同舟云学术

Video Saliency Forecasting Transformer

Published:2022-10 Issue:10 Volume:32 Page:6850-6862
ISSN:1051-8215
Container-title:IEEE Transactions on Circuits and Systems for Video Technology
language:
Short-container-title:IEEE Trans. Circuits Syst. Video Technol.

Author:

Ma Cheng¹^ORCID,Sun Haowen¹,Rao Yongming¹^ORCID,Zhou Jie¹^ORCID,Lu Jiwen¹^ORCID

Affiliation:

1. Beijing National Research Center for Information Science and Technology (BNRist) and the Department of Automation, Tsinghua University, Beijing, China

Funder

National Natural Science Foundation of China

Beijing Academy of Artificial Intelligence

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

http://xplorestaging.ieee.org/ielx7/76/9910246/09770033.pdf?arnumber=9770033

Reference70 articles.

1. DeepVS: A Deep Learning Based Video Saliency Prediction Approach

2. Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

3. Cross-View Gait Recognition Using Pairwise Spatial Transformer Networks

4. SalSAC: A Video Saliency Prediction Model with Shuffled Attentions and Correlation-Based ConvLSTM

5. Recurrent mixture density network for spatiotemporal visual attention;bazzani;arXiv 1603 08199,2016

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