happyharrycn/adaperformer

Snapshot of the Pytorch implementation of “Adaptive Perception Transformer for Temporal Action Localization”

AdaPerFormer: Adaptive Perception Transformer for Temporal Action Localization

Introduction

This code repo implements AdaPerFormer, described in the technical report: AdaPerFormer The complete code will be made public after the accepted paper.

Updates logs

[2022.8.25] Update the arxiv URL. [2022.8.25] Update the project details.

Code Overview

The main components of this project are：

• ./configs: dataset config.
• ./datasets: Data loader and IO module.
• ./model: Our main model with all its building blocks.
• ./src: Startup script, including train and test.
• ./utils: Utility functions for training, inference and other utils.

Requirements

• Linux
• Python >= 3.5
• CUDA >= 11.0
• GCC >= 4.9
• Other requirements:

    pip install -r requirement.txt

Data Preparation

1. Download the original video data from thuoms and use the I3D backbone to extract the features.
2. Place I3D_features into the folder ./data

• The folder structure should look like follows:

This folder
│   README.md
│   ...  
│
└───data/
│    └───thumos14/
│    │	 └───i3d_features   
│    │	 └───annotations   
│    └───...