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Multimodal ML models can process data in multiple modalities (e.g., video,images, audio, text) and are useful for video content analysis in a variety ofproblems (e.g., object detection, scene understanding). In this paper, we focuson the problem of video categorization by using a multimodal approach. We havedeveloped a novel temporal coherence-based regularization approach, whichapplies to different types of models (e.g., RNN, NetVLAD, Transformer). Wedemonstrate through experiments how our proposed multimodal videocategorization models with temporal coherence out-perform strongstate-of-the-art baseline models.

Exploiting Temporal Coherence for Multi-modal Video Categorization