Relation-aware Hierarchical Attention Framework for Video Question Answering

TL;DR

This paper introduces a Relation-aware Hierarchical Attention framework for VideoQA that models static and dynamic object relations in videos, improving understanding and answering accuracy.

Contribution

The novel RHA framework effectively captures both static and dynamic relations among objects, enhancing VideoQA performance over existing methods.

Findings

RHA outperforms state-of-the-art methods on large-scale VideoQA dataset.

Hierarchical attention effectively fuses multimodal features.

Dynamic relation modeling improves understanding of video content.

Abstract

Video Question Answering (VideoQA) is a challenging video understanding task since it requires a deep understanding of both question and video. Previous studies mainly focus on extracting sophisticated visual and language embeddings, fusing them by delicate hand-crafted networks. However, the relevance of different frames, objects, and modalities to the question are varied along with the time, which is ignored in most of existing methods. Lacking understanding of the the dynamic relationships and interactions among objects brings a great challenge to VideoQA task. To address this problem, we propose a novel Relation-aware Hierarchical Attention (RHA) framework to learn both the static and dynamic relations of the objects in videos. In particular, videos and questions are embedded by pre-trained models firstly to obtain the visual and textual features. Then a graph-based relation encoder is utilized to extract the static relationship between visual objects. To capture the dynamic changes of multimodal objects in different video frames, we consider the temporal, spatial, and semantic relations, and fuse the multimodal features by hierarchical attention mechanism to predict the answer. We conduct extensive experiments on a large scale VideoQA dataset, and the experimental results demonstrate that our RHA outperforms the state-of-the-art methods.