# Object State Change Classification in Egocentric Videos using the Divided Space-Time Attention Mechanism

arXiv: https://arxiv.org/abs/2207.11814 · HTML: https://mlsanity.com/paper/2207.11814/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 8 references that are in the map.
- First posted on arXiv 24 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (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)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)

## Built on by (most cited first, 4 of 4)

- [Egocentric Video Task Translation](https://mlsanity.com/paper/2212.06301/) (2022)
- [EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens](https://mlsanity.com/paper/2211.10636/) (2022)
- [Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022](https://mlsanity.com/paper/2211.15286/) (2022)
- [Chronological Blindness: Benchmarking Temporal Reasoning in Vision-Language Models with CHRONOSIGHT](https://mlsanity.com/paper/2606.16334/) (2026)

## Abstract (arXiv)

This report describes our submission called "TarHeels" for the Ego4D: Object State Change Classification Challenge. We use a transformer-based video recognition model and leverage the Divided Space-Time Attention mechanism for classifying object state change in egocentric videos. Our submission achieves the second-best performance in the challenge. Furthermore, we perform an ablation study to show that identifying object state change in egocentric videos requires temporal modeling ability. Lastly, we present several positive and negative examples to visualize our model's predictions. The code is publicly available at: https://github.com/md-mohaiminul/ObjectStateChange