Pivot Correlational Neural Network for Multimodal Video Categorization

This paper considers an architecture for multimodal video categorization referred to as Pivot Correlational Neural Network (Pivot CorrNN). The architecture consists of modal-specific streams dedicated exclusively to one specific modal input as well as modal-agnostic pivot stream that considers all modal inputs without distinction, and the architecture tries to refine the pivot prediction based on modal-specific predictions. The Pivot CorrNN consists of three modules: (1) maximizing pivot-correlation module that maximizes the correlation between the hidden states as well as the predictions of the modal-agnostic pivot stream and modal-specific streams in the network, (2) contextual Gated Recurrent Unit (cGRU) module which extends the capability of a generic GRU to take multimodal inputs in updating the pivot hidden-state, and (3) adaptive aggregation module that aggregates all modal-specific predictions as well as the modal-agnostic pivot predictions into one final prediction. We evaluate the Pivot CorrNN on two publicly available large-scale multimodal video categorization datasets, FCVID and YouTube-8M. From the experimental results, Pivot CorrNN achieves the best performance on the FCVID database and performance comparable to the state-of-the-art on YouTube-8M database.