AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation

Last update: Dec 13, 2022

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Overview

AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation

A pytorch-version implementation codes of paper: "AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation", which is accepted in BMVC 2021.

Prerequisites

Python 3.8
Pytorch 1.9
fvcore
numpy, pandas, matplotlib, tensorboardX etc.

Download Features

3D Resnet-50 features extracted from rescaled videos of ActivityNet-1.3 can be downloaded [here](will be updated soon).

Training and Testing of AEI

Default configurations of AEI are stored in config/defaults.py. The modified configurations are stored in config/*.yaml for training and testing of AEI on different datasets (ActivityNet-1.3 and THUMOS-14). We can also modify configurations through commandline arguments.

To train AEI on TAPG task of ActivityNet-1.3 with 1 GPU:

python main.py --cfg-file config/anet_proposals.yaml MODE 'training' GPU_IDS [0]

To evaluate AEI on validation set of ActivityNet-1.3 with 1 GPU:

python main.py --cfg-file config/anet_proposals.yaml MODE 'validation' GPU_IDS [0]

Reference

This implementation is partly based on this pytorch-implementation of BMN.

paper:[AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation](will update soon)

AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation

Related tags

Overview

AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation

Prerequisites

Download Features

Training and Testing of AEI

Reference

Owner

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