Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs

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Deep hierarchical parsing for semantic segmentation (2015, methodology): “CRF based image models have been quite successful in jointly modeling the appearance and structure of an image; [7, 16, 15, 14] use CRFs to combine unary potentials obtained from the visual features of super-pixels with the neighborhood constraints.”
STFCN: Spatio-Temporal FCN for Semantic Video Segmentation (2016, methodology): “The support vector machine (SVM) [43] or a Markov random ﬁeld (MRF) [38,46] are further methods used for segmentation but not as popular as the conditional random ﬁeld (CRF) that is in widespread use in recent work[5,29,35].”
Human vs. Computer in Scene and Object Recognition (2014, unknown): “Several previous studies, under the names of humans in the loop, human debugging, finding weak links (and often using Amazon Mechanical Turk) [27, 26, 36, 28, 34], have used humans to estimate the relative strengths and weaknesses of different algorithm components (e.g., [18, 36]).”