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CAIN (AAAI'2020)

Channel Attention Is All You Need for Video Frame Interpolation

Task: Video Interpolation

Abstract

Prevailing video frame interpolation techniques rely heavily on optical flow estimation and require additional model complexity and computational cost; it is also susceptible to error propagation in challenging scenarios with large motion and heavy occlusion. To alleviate the limitation, we propose a simple but effective deep neural network for video frame interpolation, which is end-to-end trainable and is free from a motion estimation network component. Our algorithm employs a special feature reshaping operation, referred to as PixelShuffle, with a channel attention, which replaces the optical flow computation module. The main idea behind the design is to distribute the information in a feature map into multiple channels and extract motion information by attending the channels for pixel-level frame synthesis. The model given by this principle turns out to be effective in the presence of challenging motion and occlusion. We construct a comprehensive evaluation benchmark and demonstrate that the proposed approach achieves outstanding performance compared to the existing models with a component for optical flow computation.

Results and models

Evaluated on RGB channels. The metrics are PSNR / SSIM . The learning rate adjustment strategy is Step LR scheduler with min_lr clipping.

Model Dataset PSNR SSIM Training Resources Download
cain_b5_g1b32_vimeo90k_triplet vimeo90k-T 34.6010 0.9578 1 (Tesla V100-SXM2-32GB) model/log

Quick Start

Train

Train Instructions

You can use the following commands to train a model with cpu or single/multiple GPUs.

# cpu train
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py

# single-gpu train
python tools/train.py configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py

# multi-gpu train
./tools/dist_train.sh configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py 8

For more details, you can refer to Train a model part in train_test.md.

Test

Test Instructions

You can use the following commands to test a model with cpu or single/multiple GPUs.

# cpu test
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth

# single-gpu test
python tools/test.py configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth

# multi-gpu test
./tools/dist_test.sh configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth 8

For more details, you can refer to Test a pre-trained model part in train_test.md.

Citation

@inproceedings{choi2020channel,
  title={Channel attention is all you need for video frame interpolation},
  author={Choi, Myungsub and Kim, Heewon and Han, Bohyung and Xu, Ning and Lee, Kyoung Mu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={07},
  pages={10663--10671},
  year={2020}
}