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Adaptive occlusion sensitivity analysis

This repository contains the implementation of the paper "Visually explaining 3D-CNN predictions for video classification with an adaptive occlusion sensitivity analysis".

This repository includes the work that is distributed in the Apache License 2.0.

Requirements

Instalation

Download model parameters

  1. Download the parameters of R3D fine-tuned on UCF-101 from here
  2. Place the downloaded file (save_200.pth) into data/r3d_models/finetuning/ucf101/r3d50_K_fc/

Create docker container

  1. $cd .devcontainer
  2. $docker-compose up -d
  3. $docker attach aosa

Example

Please refer to occlusion_sensitivity_analysis.ipynb.

If there is no enough GPU memory, please try to small "batchsize" in the example codes.

Models and dataset utils

We use the code from the following repository for 3D-CNN models and dataset utilities. To download datasets and other resources, please refer to this repository.

kenshohara/3D-ResNets-PyTorch: 3D ResNets for Action Recognition (CVPR 2018)

Citation

@article{uchiyama2022visually,
  title = {{Visually explaining 3D-CNN predictions for video classification with an adaptive occlusion sensitivity analysis}},
  author = {Uchiyama, Tomoki and Sogi, Naoya and Niinuma, Koichiro and Fukui, Kazuhiro},
  journal={arXiv preprint arXiv:2207.12859},
  year = {2022}
}