publication . Conference object . 2019

Deep Spatio-Temporal Modeling for Object-Level Gaze-Based Relevance Assessment

Konstantinos Stavridis; Athanasios Psaltis; Anastasios Dimou; Georgios Th. Papadopoulos; Petros Daras;
Open Access
  • Published: 18 Nov 2019
  • Publisher: IEEE
The current work investigates the problem of objectlevel relevance assessment prediction, taking into account the user’s captured gaze signal (behaviour) and following the Deep Learning (DL) paradigm. Human gaze, as a sub-conscious response, is influenced from several factors related to the human mental activity. Several studies have so far proposed methodologies based on the use of gaze statistical modeling and naive classifiers for assessing images or image patches as relevant or not to the user’s interests. Nevertheless, the outstanding majority of literature approaches only relied so far on the use of handcrafted features and relative simple classification s...
Persistent Identifiers
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Gaze modeling, Deep learning, Relevance assessment, Discriminant, Object level, Statistical model, Key factors, Machine learning, computer.software_genre, computer, Computer science, Artificial intelligence, business.industry, business, Gaze, Deep learning, Classification scheme, Temporal modeling
Funded by
Advanced tools for fighting oNline Illegal TrAfficking
  • Funder: European Commission (EC)
  • Project Code: 787061
  • Funding stream: H2020 | RIA
Validated by funder
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