Action-Centric Relation Transformer Network for Video Question Answering

Video question answering (VideoQA) has emerged as a popular research topic in recent years. Enormous efforts have been devoted to developing more effective fusion strategies and better intra-modal feature preparation. To explore these issues further, we identify two key problems. (1) Current works take almost no account of introducing action of interest in video representation. Additionally, there exists insufficient labeling data on where the action of interest is in many datasets. However, questions in VideoQA are usually action-centric. (2) Frame-to-frame relations, which can provide useful temporal attributes (e.g., state transition, action counting), lack relevant research. Based on these observations, we propose an action-centric relation transformer network (ACRTransformer) for VideoQA and make two significant improvements. (1) We explicitly consider the action recognition problem and present a visual feature encoding technique, action-based encoding (ABE), to emphasize the frames with high actionness probabilities (the probability that the frame has actions). (2) We better exploit the interplays between temporal frames using a relation transformer network (RTransformer). Experiments on popular benchmark datasets in VideoQA clearly establish our superiority over previous state-of-the-art models. Code could be found at https://github.com/op-multimodal/ACRTransformer.

Automated summary

Video question answering (VideoQA) is a popular research topic that has received a lot of attention in recent years. Researchers have focused on fusion strategies and feature preparation, but little attention has been given to incorporating actions of interest and exploring frame-to-frame relations. This study introduces an action-centric relation transformer network (ACRTransformer) that addresses these issues and demonstrates superior performance over previous state-of-the-art models.