This is the official PyTorch implementation of Higher Order Recurrent Space-Time Transformer. The complete training codes to reproduce the results will be released soon.
import torch
from horst import HORST
model = HORST(
input_channels=256,
layers_per_block=[2],
hidden_channels=[512],
stride=[1],
)
input = torch.randn(1, 8, 256, 56, 56) # (Batch, Timesteps, Channels, Height, Width)
out = model(input) # (1, 8, 512, 56, 56)@misc{tai2021higher,
title={Higher Order Recurrent Space-Time Transformer},
author={Tsung-Ming Tai and Giuseppe Fiameni and Cheng-Kuang Lee and Oswald Lanz},
year={2021},
eprint={2104.08665},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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