# VideoLightFormer: Lightweight Action Recognition using Transformers

arXiv: https://arxiv.org/abs/2107.00451 · HTML: https://mlsanity.com/paper/2107.00451/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 27 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [The "something something" video database for learning and evaluating visual common sense](https://mlsanity.com/paper/1706.04261/) (2017)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Video Transformer Network](https://mlsanity.com/paper/2102.00719/) (2021)
- [Only Time Can Tell: Discovering Temporal Data for Temporal Modeling](https://mlsanity.com/paper/1907.08340/) (2019)
- [VideoLSTM Convolves, Attends and Flows for Action Recognition](https://mlsanity.com/paper/1607.01794/) (2016)
- [Action Recognition using Visual Attention](https://mlsanity.com/paper/1511.04119/) (2015)
- [AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification](https://mlsanity.com/paper/2007.12034/) (2020)
- [Knowing What, Where and When to Look: Efficient Video Action Modeling with Attention](https://mlsanity.com/paper/2004.01278/) (2020)

## Built on by (most cited first, 3 of 3)

- [Vision Transformers for Action Recognition: A Survey](https://mlsanity.com/paper/2209.05700/) (2022)
- [An Efficient Spatio-Temporal Pyramid Transformer for Action Detection](https://mlsanity.com/paper/2207.10448/) (2022)
- [Recur, Attend or Convolve? On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognition](https://mlsanity.com/paper/2112.12175/) (2021)

## Abstract (arXiv)

Efficient video action recognition remains a challenging problem. One large model after another takes the place of the state-of-the-art on the Kinetics dataset, but real-world efficiency evaluations are often lacking. In this work, we fill this gap and investigate the use of transformers for efficient action recognition. We propose a novel, lightweight action recognition architecture, VideoLightFormer. In a factorized fashion, we carefully extend the 2D convolutional Temporal Segment Network with transformers, while maintaining spatial and temporal video structure throughout the entire model. Existing methods often resort to one of the two extremes, where they either apply huge transformers to video features, or minimal transformers on highly pooled video features. Our method differs from them by keeping the transformer models small, but leveraging full spatiotemporal feature structure. We evaluate VideoLightFormer in a high-efficiency setting on the temporally-demanding EPIC-KITCHENS-100 and Something-Something-V2 (SSV2) datasets and find that it achieves a better mix of efficiency and accuracy than existing state-of-the-art models, apart from the Temporal Shift Module on SSV2.