A Better Way to Attend: Attention with Trees for Video Question Answering

TL;DR

This paper introduces a novel syntax-aware attention model using tree structures for improved video question answering, especially on complex questions, by leveraging sentence parse trees and hierarchical attention mechanisms.

Contribution

The paper proposes the HTreeMN model that incorporates sentence parse trees into attention mechanisms, enhancing understanding of complex questions in video QA tasks.

Findings

Outperforms existing attention models on complex questions

Effective utilization of sentence parse trees improves accuracy

Hierarchical attention distills relevant features efficiently

Abstract

We propose a new attention model for video question answering. The main idea of the attention models is to locate on the most informative parts of the visual data. The attention mechanisms are quite popular these days. However, most existing visual attention mechanisms regard the question as a whole. They ignore the word-level semantics where each word can have different attentions and some words need no attention. Neither do they consider the semantic structure of the sentences. Although the Extended Soft Attention (E-SA) model for video question answering leverages the word-level attention, it performs poorly on long question sentences. In this paper, we propose the heterogeneous tree-structured memory network (HTreeMN) for video question answering. Our proposed approach is based upon the syntax parse trees of the question sentences. The HTreeMN treats the words differently where the \textit{visual} words are processed with an attention module and the \textit{verbal} ones not. It also utilizes the semantic structure of the sentences by combining the neighbors based on the recursive structure of the parse trees. The understandings of the words and the videos are propagated and merged from leaves to the root. Furthermore, we build a hierarchical attention mechanism to distill the attended features. We evaluate our approach on two datasets. The experimental results show the superiority of our HTreeMN model over the other attention models especially on complex questions. Our code is available on github. Our code is available at https://github.com/ZJULearning/TreeAttention