Implementation of STAM (Space Time Attention Model), yet another pure and simple SOTA attention model that bests all previous models in video classification. This corroborates the finding of TimeSformer. Attention is all we need.
$ pip install stam-pytorchimport torch
from stam_pytorch import STAM
model = STAM(
dim = 512,
image_size = 256, # size of image
patch_size = 32, # patch size
num_frames = 5, # number of image frames, selected out of video
space_depth = 12, # depth of vision transformer
space_heads = 8, # heads of vision transformer
space_mlp_dim = 2048, # feedforward hidden dimension of vision transformer
time_depth = 6, # depth of time transformer (in paper, it was shallower, 6)
time_heads = 8, # heads of time transformer
time_mlp_dim = 2048, # feedforward hidden dimension of time transformer
num_classes = 100, # number of output classes
space_dim_head = 64, # space transformer head dimension
time_dim_head = 64, # time transformer head dimension
dropout = 0., # dropout
emb_dropout = 0. # embedding dropout
)
frames = torch.randn(2, 5, 3, 256, 256) # (batch x frames x channels x height x width)
pred = model(frames) # (2, 100)@misc{sharir2021image,
title = {An Image is Worth 16x16 Words, What is a Video Worth?},
author = {Gilad Sharir and Asaf Noy and Lihi Zelnik-Manor},
year = {2021},
eprint = {2103.13915},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}