# Expanded Parts Model for Semantic Description of Humans in Still Images

arXiv: https://arxiv.org/abs/1509.04186 · HTML: https://mlsanity.com/paper/1509.04186/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2015.

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

- [Open Set Action Recognition via Multi-Label Evidential Learning](https://mlsanity.com/paper/2303.12698/) (2023)
- [Regional Attention Network (RAN) for Head Pose and Fine-grained Gesture Recognition](https://mlsanity.com/paper/2101.06634/) (2021)
- [Fight Detection from Still Images in the Wild](https://mlsanity.com/paper/2111.08370/) (2021)

## Abstract (arXiv)

We introduce an Expanded Parts Model (EPM) for recognizing human attributes (e.g. young, short hair, wearing suit) and actions (e.g. running, jumping) in still images. An EPM is a collection of part templates which are learnt discriminatively to explain specific scale-space regions in the images (in human centric coordinates). This is in contrast to current models which consist of a relatively few (i.e. a mixture of) 'average' templates. EPM uses only a subset of the parts to score an image and scores the image sparsely in space, i.e. it ignores redundant and random background in an image. To learn our model, we propose an algorithm which automatically mines parts and learns corresponding discriminative templates together with their respective locations from a large number of candidate parts. We validate our method on three recent challenging datasets of human attributes and actions. We obtain convincing qualitative and state-of-the-art quantitative results on the three datasets.