# Hierarchical Spatial Sum-Product Networks for Action Recognition in Still Images

arXiv: https://arxiv.org/abs/1511.05292 · HTML: https://mlsanity.com/paper/1511.05292/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2015.

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

- [Attention in Attention: Modeling Context Correlation for Efficient Video Classification](https://mlsanity.com/paper/2204.09303/) (2022)
- [On the Expressive Power of Tree-Structured Probabilistic Circuits](https://mlsanity.com/paper/2410.05465/) (2024)
- [Incremental Class Learning using Variational Autoencoders with Similarity Learning](https://mlsanity.com/paper/2110.01303/) (2021)

## Abstract (arXiv)

Recognizing actions from still images is popularly studied recently. In this paper, we model an action class as a flexible number of spatial configurations of body parts by proposing a new spatial SPN (Sum-Product Networks). First, we discover a set of parts in image collections via unsupervised learning. Then, our new spatial SPN is applied to model the spatial relationship and also the high-order correlations of parts. To learn robust networks, we further develop a hierarchical spatial SPN method, which models pairwise spatial relationship between parts inside sub-images and models the correlation of sub-images via extra layers of SPN. Our method is shown to be effective on two benchmark datasets.