# STAIR Actions: A Video Dataset of Everyday Home Actions

arXiv: https://arxiv.org/abs/1804.04326 · HTML: https://mlsanity.com/paper/1804.04326/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

## Built on by (most cited first, 6 of 6)

- [Revisiting spatio-temporal layouts for compositional action recognition](https://mlsanity.com/paper/2111.01936/) (2021)
- [MASTAF: A Model-Agnostic Spatio-Temporal Attention Fusion Network for Few-shot Video Classification](https://mlsanity.com/paper/2112.04585/) (2021)
- [Unsupervised Action Localization Crop in Video Retargeting for 3D ConvNets](https://mlsanity.com/paper/2111.07426/) (2021)
- [Understanding in Artificial Intelligence](https://mlsanity.com/paper/2101.06573/) (2021)
- [Building a Video-and-Language Dataset with Human Actions for Multimodal Logical Inference](https://mlsanity.com/paper/2106.14137/) (2021)
- [Action Class Relation Detection and Classification Across Multiple Video Datasets](https://mlsanity.com/paper/2308.07558/) (2023)

## Abstract (arXiv)

A new large-scale video dataset for human action recognition, called STAIR Actions is introduced. STAIR Actions contains 100 categories of action labels representing fine-grained everyday home actions so that it can be applied to research in various home tasks such as nursing, caring, and security. In STAIR Actions, each video has a single action label. Moreover, for each action category, there are around 1,000 videos that were obtained from YouTube or produced by crowdsource workers. The duration of each video is mostly five to six seconds. The total number of videos is 102,462. We explain how we constructed STAIR Actions and show the characteristics of STAIR Actions compared to existing datasets for human action recognition. Experiments with three major models for action recognition show that STAIR Actions can train large models and achieve good performance. STAIR Actions can be downloaded from http://actions.stair.center