Learning to Cut by Watching Movies

Official Code of ICCV 2021 Paper: Learning to Cut by Watching Movies

[ ArXiv | Project Website | ICCV2021 ]

Learning to Cut by Watching Movies. Alejandro Pardo*, Fabian Caba Heilbron, Juan León Alcázar, Ali Thabet, Bernard Ghanem. In ICCV, 2021.

Installation

Clone the repository and move to folder:

git clone https://github.com/PardoAlejo/LearningToCut.git
cd LearningToCut

Install environmnet:

conda env create -f ltc-env.yml

Data

Download the following resources and extract the content in the appropriate destination folder. See table.

Resource	Drive File	Destination Folder
Train Annotations	link	./data/
Val Annotations	link	./data/
Video Durations	link	./data/
Video Features	link	./data/
Audio Features	link	./data/
Best Model	link	./checkpoints/

If you want to extract features yourself, or you need the original videos instead, please refer to data/DATA.md

The folder structure should be as follows:

README.md
ltc-env.yml
│
├── data
│   ├── ResNexT-101_3D_video_features.h5
│   ├── ResNet-18_audio_features.h5
│   ├── subset_moviescenes_shotcuts_train.csv
│   ├── subset_moviescenes_shotcuts_val.csv
│   └── durations.csv
│
├── checkpoints
|    ├── best_state.ckpt
│
└── scripts

Inference

Copy paste the following commands in the terminal.

Load environment:

conda activate ltc
cd scripts/

Inference on val set

sh inference.sh

Expected results (Table 1 of the Paper):

Method	AR1-D1	AR3-D1	AR5-D1	AR10-D1	AR1-D2	AR3-D2	AR5-D2	AR10-D2	AR1-D3	AR3-D3	AR5-D3	AR10-D3
Random	0.64%	1.91%	3.15%	6.28%	1.85%	5.65%	9.32%	18.52%	3.67%	10.67%	17.62%	33.91%
Raw	1.16%	3.97%	6.36%	11.72%	2.51%	8.32%	13.15%	24.25%	3.73%	12.19%	19.33%	34.97%
LTC	8.18%	17.95%	24.44%	30.35%	15.30%	35.11%	48.26%	59.42%	19.18%	46.32%	64.30%	79.35%

Cite us

@InProceedings{Pardo_2021_ICCV,
    author    = {Pardo, Alejandro and Caba, Fabian and Alcazar, Juan Leon and Thabet, Ali K. and Ghanem, Bernard},
    title     = {Learning To Cut by Watching Movies},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6858-6868}
}