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adascan-public's Introduction

AdaScan

This repository contains the source code for the paper Adascan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos, Amlan Kar* (IIT Kanpur), Nishant Rai* (IIT Kanpur), Karan Sikka (UCSD and SRI), Gaurav Sharma (IIT Kanpur), with support for multi-GPU training and testing.

Dependencies

Note: skimage and skvideo are required for the preprocessing step

Setup

  • Download UCF-101 dataset from here and UCF-101 flow files from here
  • Download UCF-101 action recognition splits from here (to be passed using -split_dir)
  • Run preprocessing script to create npz files required for training/testing (directory created to be passed using -data_dir)

Training from scratch

  • [RGB training] Download VGG numpy files from here (to be passed using -vgg_npy_path)
  • [Optical Flow training] Download the pre-trained caffe models for flow from here and convert them using this tool to numpy files
  • Edit sample_train.sh and run

Testing pre-trained models or self-trained models

  • Download the pre-trained models from the given links below
  • Download VGG numpy file for RGB and any one of the flow files to pass with -npy_path for testing (This is an extra step and doesn't change anything, we will remove this unneccessary step soon)
  • Edit sample_test.sh and run

Visualizing on custom video (only for RGB)

python demo.py -ckpt_file path/to/ckpt/file -vid_file vis/vid_file

This should save an image in vis/ that looks like:

Sample visualization

Pre-trained models (Coming Soon)

These models have been trained on UCF-101. We will be releasing the updated models soon.

RGB

Flow

Training/Testing

Sample self explanatory train and test scripts have been provided with the code

Updated Results

After fixing a bug post-submission, we have achieved higher results with the same configuration as in the original paper. We request authors to cite these numbers.

Model UCF-101 HMDB-51
AdaScan 91.6 62.4
AdaScan + iDT 93.1 67.6
AdaScan + iDT + C3D 94.0 69.4

Reference

If you use this code as part of any published research, please acknowledge the following paper:

AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos
Amlan Kar*, Nishant Rai*, Karan Sikka, Gaurav Sharma (*denotes equal contribution)

@article{kar2016adascan,
title={AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos},
author={Kar, Amlan and Rai, Nishant and Sikka, Karan and Sharma, Gaurav},
booktitle={CVPR}, 
year={2017} 
}  

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adascan-public's Issues

unable to find file

Hello! the VGG numpy files link is 404,unable to find file!And initialize flow models link is the caffe model ,but The project uses tensorflow! thank you !!

Link to VGG numpy files is lost

Thanks for your wonderful code. I met a problem when I try to train from scratch. The link to VGG numpy files seems to be lost. Could you please check it?

About the difference with the paper

Hi:

I find the model somehow separates the vgg for RGB and flow apart, while in the paper it seems that there are two vggs, and the RGB and flow sequence go through them and are concated together in a 4096 * 25 matrix. Does that means that the following MLP and classifier only take the input of either RGB or flow into account?

Thanks!

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