Hierarchical Temporal Fusion of Multi-grained Attention Features for Video Question Answering

This work aims to address the problem of video question answering (VideoQA) with a novel model and a new open-ended VideoQA dataset. VideoQA is a challenging field in visual information retrieval, which aims to generate the answer according to the video content and question. Ultimately, VideoQA is a video understanding task. Efficiently combining the multi-grained representations is the key factor in understanding a video. The existing works mostly focus on overall frame-level visual understanding to tackle the problem, which neglects finer-grained and temporal information inside the video, or just combines the multi-grained representations simply by concatenation or addition. Thus, we propose the multi-granularity temporal attention network that enables to search for the specific frames in a video that are holistically and locally related to the answer. We first learn the mutual attention representations of multi-grained visual content and question. Then the mutually attended features are combined hierarchically using a double layer LSTM to generate the answer. Furthermore, we illustrate several different multi-grained fusion configurations to prove the advancement of this hierarchical architecture. The effectiveness of our model is demonstrated on the large-scale video question answering dataset based on ActivityNet dataset.