Video Dialog via Multi-Grained Convolutional Self-Attention Context Multi-Modal Networks

Video dialog is a new and challenging task, which requires an AI agent to maintain a meaningful dialog with humans in natural language about video contents. Specifically, given a video, a dialog history and a new question about the video, the agent has to combine video information with dialog history to infer the answer. However, the existing methods of image dialog and video question answering, which fail to process the complexity of video information and establish the logical dependency of history contexts, are inappropriate to be applied directly to video dialog. In this paper, we propose a novel approach for video dialog called multi-grained convolutional self-attention context network, which combines video information with dialog history. Instead of using RNN to encode the sequence information, we design a multi-grained convolutional self-attention mechanism to capture both element and segment level interactions that contain multi-grained sequence information. Moreover, a hierarchical dialog history encoder is designed to learn the context-aware question representation. Finally, we establish two decoders in multiple-choice and open-ended forms respectively, which utilize different strategies to get the multi-model context-aware video representation and to generate human-like answers. We evaluate our method on two large-scale datasets. Due to the flexibility and parallelism of the new attention mechanism, our method can achieve higher time efficiency, and the extensive experiments also show the effectiveness of our method.