# Improving Human Action Recognition by Non-action Classification

arXiv: https://arxiv.org/abs/1604.06397 · HTML: https://mlsanity.com/paper/1604.06397/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2016.

## Built on by (most cited first, 1 of 1)

- [Leveraging Vision-Language Large Models for Interpretable Video Action Recognition with Semantic Tokenization](https://mlsanity.com/paper/2509.05695/) (2025)

## Abstract (arXiv)

In this paper we consider the task of recognizing human actions in realistic video where human actions are dominated by irrelevant factors. We first study the benefits of removing non-action video segments, which are the ones that do not portray any human action. We then learn a non-action classifier and use it to down-weight irrelevant video segments. The non-action classifier is trained using ActionThread, a dataset with shot-level annotation for the occurrence or absence of a human action. The non-action classifier can be used to identify non-action shots with high precision and subsequently used to improve the performance of action recognition systems.