Hierarchical kernel stick-breaking process for multi-task image analysis

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An Overview of Multi-task Learning (2018, methodology): “Applications of MTL based on non-facial images include object categorization [86], image segmentation [89,90], identifying brain imaging predictors [91], saliency detection [92], action recognition [93], scene classification [94], multi-attribute prediction [95], multi-camera person…”
A Survey on Multi-Task Learning (2017, methodology): “atics, health informatics, speech, natural language processing, web applications, ubiquitous computing, and so on. In the following, we introduce them in a chronological order. 5.1 Computer Vision In [163], a hierarchical kernel stick-breaking process is proposed for multiple image segmentation wher”
A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation (2009, methodology): “Moreover, recent work has also used the Dirichlet process (DP) [5] or similar non-parametric priors to enhance the topic-model structure [2, 20, 26].”
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning (2016, background): “Some recent applications include computational genetics [63], image segmentation [1], HIV therapy screening [14], collaborative ﬁltering [18], age estimation from facial images [67], and sub-cellular location prediction [64], just to name a few prominent ones.”