Multimodal Machine Learning: A Survey and Taxonomy

0162-8828 (c) 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI , IEEET ransactions on Pattern Analysis and Machine IntelligenceTRANSACTIONS OF PATTERN ANALYSIS AND Machine INTELLIGENCE1 Multimodal Machine Learning: A Survey and TaxonomyTadas Baltru saitis, Chaitanya Ahuja, and Louis-Philippe MorencyAbstract Our experience of the world is Multimodal - we see objects, hear sounds, feel texture, smell odors, and taste to the way in which something happens or is experienced and a research problem is characterized asmultimodalwhenit includes multiple such modalities.

of the first audio-visual emotion challenge (AVEC) orga-nized in 2011 [186]. The fields of emotion recognition and affective computing bloomed in the early 2010s thanks to strong technical advances in automatic face detection, facial landmark detection, and facial expression recognition [48]. The AVEC challenge continued annually afterward ...

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  Expression, Emotions, Recognition, Facial, Facial expression recognition, Emotion recognition

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