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Online Cross-Modal Adaptation for Audio–Visual Person Identification With Wearable Cameras
IEEE Transactions on Human-Machine Systems
We propose an audio-visual target identification approach for egocentric data with cross-modal model adaptation. The proposed approach blindly and iteratively adapts the timedependent models of each modality to varying target appearance and environmental conditions using the posterior of the other modality. The adaptation is unsupervised and performed on-line, thus models can be improved as new unlabelled data become available. In particular, accurate models do not deteriorate when a modalitydoi:10.1109/thms.2016.2620110 fatcat:goygftl3dfdcbmpvk2vuzbu5mi