No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion

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An Overview of Multi-task Learning (2018, background): “Specifically, applications of MTL based on facial images include face verification [84], personalized age estimation [85], multi-cue face recognition [86], head-pose estimation [22,87], facial landmark detection [18], and facial image rotation [88].”
A Survey on Multi-Task Learning (2017, neutral): “In [173], a multi-task decomposition model, where each component matrix is regularized by a priori information organized in a graph, is proposed for head pose classification in an uncontrolled environment.”
Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusion (2016, background): “deal with more complex scenarios involving tens of participants [44], [45].”
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection (2015, methodology): “Future works include investigating other network architectures, alternative approaches for fusing multimodal data in the context of SDAE, and extending our framework using multi-task learning [34, 35] for detecting anomalies in heterogeneous scenes.”