TreeAttention

Table of Contents

• Introduction
• Performance
  • Datasets
  • Compared Algorithms
  • Results
• Building Instruction
  • Prerequisites
• Usage
• Reference
• License

Introduction

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

The HTreeMN model is a tree-structured attention neural network based on the syntactic parse tree of the natural language sentence. Each node of the tree-structured network does its computation based on the property of the corresponding word or intermediate representation.

For a faster partially batched version of the model, see BatchedTreeLSTM

Performance

Datasets

• VideoQA Dataset

Compared Algorithms

• [E-SA] (https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/viewFile/14906/14319)
• [E-SS] (https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/viewFile/14906/14319)
• Simple: a designed based-line, which does not utilize attention mechanisms.

Results

HTreeMN achieves the best results. Its performance does not drop as the length of question increases.

Building Instruction

Prerequisites

• Python 3.0+
• Pytorch 0.4.0+

Usage

• Packaging the datasets into python pickle files and run python main.py

Reference

If you use our work, please cite our paper,

@article{xue2018tree,

title={A Better Way to Attend: Attention With Trees for Video Question Answering},

author={Xue, Hongyang and Chu, Wenqing and Zhao, Zhou and Cai, Deng},

journal={IEEE Transactions on Image Processing},

year={2018},

publisher={IEEE}

}