Multi-class multi-annotator active learning with robust Gaussian process for visual recognition

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 2839-2847

  Long, Chengjiang ^a   Hua, Gang ^b  

^a STEVENS INSTITUTE OF TECHNOLOGY   (United States)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION ALGORITHMS; BAYESIAN NETWORKS; COMPUTER VISION; ECONOMIC AND SOCIAL EFFECTS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); INFERENCE ENGINES; MAXIMUM PRINCIPLE; REINFORCEMENT LEARNING;

APPROXIMATE BAYESIAN INFERENCE; APPROXIMATION INFERENCE; EXPECTATION PROPAGATION; EXPLOITATION AND EXPLORATIONS; GAUSSIAN PROCESSES; GENERALIZED EXPECTATIONMAXIMIZATION ALGORITHM (GEM); PROBABILISTIC MODELING; VISUAL RECOGNITION;

ARTIFICIAL INTELLIGENCE;

EID: 84973922883     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.325     Document Type: Conference Paper

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