基于树结构策略的渐进式强化学习，用于视频中的时间语言基础 _python_代码_下载

# 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](https://arxiv.org/pdf/2001.06680.pdf) ## Motivation  ## Framework  ## Requirements - Python 2.7 - Pytorch 0.4.1 - matplotlib - The code is for [Charades-STA](https://arxiv.org/pdf/1705.02101.pdf) dataset. ## Visual Features Please download the features in [Features1](https://drive.google.com/drive/folders/1U1GEti3JjLfOAN0AhCb0VXqfGoKV9qMo?usp=sharing), and put it in the "Dataset/Charades" folder. ## Training and Testing Data Please download the TrainingData in [TrainingData](https://drive.google.com/file/d/14aAJf1Wgn6wHFGdMHDgZBphKG1FbZt_y/view?usp=sharing), and put it in the "Dataset/Charades/ref_info" folder. Please download the TestingData in [TestingData](https://drive.google.com/file/d/1mg2ru344tzL20iQNRCT2WAzfiHEjppLB/view?usp=sharing), 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](https://drive.google.com/open?id=1lyOlcKR5PY7cN_yjZfGUypfNRTokw_He) ## Train ### ``` python train.py ``` ## Validate ### ``` python val.py ``` ## Test from Pre-trained Model ### ``` python test.py ```