Multimodal Deep Learning for Video Classification

In this research, we created a multimodal deep learning model, which incorporates visual, audio, and textual data that can perform better than video categorization using only one modality. Previous techniques frequently had trouble in capturing the intricate interplay between modalities in data, which has been shown to be suboptimal. We use CNNs for visual content, an RNN or transformer for temporal dependencies, pre-trained embeddings for textual information, and more advanced fusion algorithms (such as attention) to merge these components where we see fit. It displays greater classification accuracy over existing baselines, attaining a 12% improved accuracy rate on video recognition tasks. As proven in the ablation study, the fusion technique we presented is critical since the findings from the single-modality models are worse and are much worse than the results from the multimodal models. This work is advantageous in a number of applications like video surveillance, human activity detection, and multimedia content analysis using real-time low-cost solutions where precise and rapid matching of films performing the same human movement is the key requirement. However, the framework can be utilized to tap into the complexity of real-life data that incorporates numerous sources in the same paradigm of spatial-temporal data following the form of source = video =(optical flow + object identification). It is a promising move towards real-life deployments of the practical side of video analysis that could stimulate new research agendas from academia and industry.