SALICON: Reducing the semantic gap in saliency prediction by adapting deep neural networks

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 262-270

  Huang, Xun ^a,b   Shen, Chengyao ^a   Boix, Xavier ^a,c   Zhao, Qi ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b BEIHANG UNIVERSITY   (China)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BEHAVIORAL RESEARCH; COMPUTER VISION; EYE MOVEMENTS; OBJECT RECOGNITION; SEMANTICS;

ARCHITECTURE-BASED; BENCHMARK DATASETS; DEEP NEURAL NETWORKS; EVALUATION METRICS; HIGH LEVEL SEMANTICS; INTEGRATING INFORMATION; OBJECTIVE FUNCTIONS; SEMANTIC CONTENT;

FORECASTING;

EID: 84973923049     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.38     Document Type: Conference Paper

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