An Image is Worth 16x16 Words, What is a Video Worth?

Official PyTorch Implementation

Gilad Sharir, Asaf Noy, Lihi Zelnik-Manor
DAMO Academy, Alibaba Group

Abstract

Leading methods in the domain of action recognition try to distill information from both the spatial and temporal dimensions of an input video. Methods that reach State of the Art (SotA) accuracy, usually make use of 3D convolution layers as a way to abstract the temporal information from video frames. The use of such convolutions requires sampling short clips from the input video, where each clip is a collection of closely sampled frames. Since each short clip covers a small fraction of an input video, multiple clips are sampled at inference in order to cover the whole temporal length of the video. This leads to increased computational load and is impractical for real-world applications. We address the computational bottleneck by significantly reducing the number of frames required for inference. Our approach relies on a temporal transformer that applies global attention over video frames, and thus better exploits the salient information in each frame. Therefore our approach is very input efficient, and can achieve SotA results (on Kinetics dataset) with a fraction of the data (frames per video), computation and latency. Specifically on Kinetics-400, we reach 78.8 top-1 accuracy with ×30 less frames per video, and ×40 faster inference than the current leading method

Update 2/5/2021: Improved results

Due to improved training hyperparameters, and using KD training, we were able to improve STAM results on Kinetics400 (+ ~1.5%). We are releasing the pretrained weights of the improved models (see Pretrained Models below).

Main Article Results

STAM models accuracy and GPU throughput on Kinetics400, compared to X3D. All measurements were done on Nvidia V100 GPU, with mixed precision. All models are trained on input resolution of 224.

Models	Top-1 Accuracy (%)	Flops × views (10^9)	# Input Frames	Runtime (Videos/sec)
X3D-M	76.0	6.2 × 30	480	1.3
X3D-L	77.5	24.8 × 30	480	0.46
X3D-XL	79.1	48.4 × 30	480	N/A
X3D-XXL	80.4	194 × 30	480	N/A
TimeSformer-L	80.7	2380 × 3	288	N/A
ViViT-L	81.3	3992 × 12	384	N/A
STAM-8	77.5	135 × 1	8	---
STAM-16	79.3	270 × 1	16	20.0
STAM-32	79.95	540 × 1	32	---
STAM-64	80.5	1080 × 1	64	4.8

Pretrained Models

We provide a collection of STAM models pre-trained on Kinetics400.

Model name	checkpoint
STAM_8	link
STAM_16	link
STAM_32	link
STAM_64	link

Reproduce Article Scores

We provide code for reproducing the validation top-1 score of STAM models on Kinetics400. First, download pretrained models from the links above.

Then, run the infer.py script. For example, for stam_16 (input size 224) run:

python -m infer \
--val_dir=/path/to/kinetics_val_folder \
--model_path=/model/path/to/stam_16.pth \
--model_name=stam_16
--input_size=224

Citations

@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}
}

Acknowledgements

We thank Tal Ridnik for discussions and comments.

Some components of this code implementation are adapted from the excellent repository of Ross Wightman. Check it out and give it a star while you are at it.

Alibaba-MIIL/STAM