Abstract. 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 pivotcorrelation 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