Weakly Supervised Visual Saliency Prediction

Author:

Zhou Lai1,Zhou Tianfei2ORCID,Khan Salman3ORCID,Sun Hanqiu4ORCID,Shen Jianbing5ORCID,Shao Ling3ORCID

Affiliation:

1. State Key Laboratory of Media Convergence and Communication, Neuroscience and Intelligent Media Institute, Communication University of China (CUC), Beijing, China

2. Computer Vision Laboratory, ETH Zürich, Zürich, Switzerland

3. Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates

4. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

5. Department of Computer and Information Science, State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China

Funder

National Natural Science Foundation of China

Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing

CCF-Baidu Open Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Reference110 articles.

1. Deep gaze I: Boosting saliency prediction with feature maps trained on imagenet;kümmerer;Proc Int Conf Learn Represent,2015

2. An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed

3. A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression;guo;IEEE Trans Image Process,2009

4. Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform;guo;Proc IEEE Conf Comput Vis Pattern Recognit,2008

5. Center-surround patterns emerge as optimal predictors for human saccade targets

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