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Tree-Structured Policy based Progressive Reinforcement Learning for Temporally Language Grounding in Video (AAAI2020)
This repository contains the pytorch codes and trained models described in the paper "Tree-Structured Policy based Progressive Reinforcement Learning for Temporally Language Grounding in Video" By Jie Wu, Guanbin Li, Si Liu, Liang Lin. Paper
Motivation
Framework
Requirements
- Python 2.7
- Pytorch 0.4.1
- matplotlib
- The code is for Charades-STA dataset.
Visual Features
Please download the features in Features1, and put it in the "Dataset/Charades" folder.
Training and Testing Data
Please download the TrainingData in TrainingData, and put it in the "Dataset/Charades/ref_info" folder. Please download the TestingData in TestingData, and put it in the "Dataset/Charades/ref_info" folder.
Pre-trained models
We provide the pre-trained model for Charades-STA dataset, which can get 24.73 on R@1, IoU0.7 and 45.30 on R@1, IoU0.5: Models
Train
python train.py
Validate
python val.py
Test from Pre-trained Model
python test.py