Hierarchical Recurrent Contextual Attention Network for Video Question Answering

Abstract

Video question answering (VideoQA) is a task of answering a natural language question related to the content of a video. Existing methods that utilize the fine-grained object information have achieved significant improvements, however, they rely on costly external object detectors or fail to explore the rich structure of videos. In this work, we propose to understand video from two dimensions: temporal and semantic. In semantic space, videos are organized in a hierarchical structure (pixels, objects, activities, events). In temporal space, video can be viewed as a sequence of events, which contain multiple objects and activities. Based on this insight, we propose a reusable neural unit called recurrent contextual attention (RCA). RCA receives a 2D grid feature and conditional features as input, and computes multiple high-order compositional semantic representations. We then stack these units to build our hierarchy and utilize recurrent attention to generate diverse representations for different views of each subsequence. Without the bells and whistles, our model achieves excellent performance on three VideoQA datasets: TGIF-QA, MSVD-QA, and MSRVTT-QA using only grid features. Visualization results further validate the effectiveness of our method.