# A spatiotemporal model with visual attention for video classification

arXiv: https://arxiv.org/abs/1707.02069 · HTML: https://mlsanity.com/paper/1707.02069/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2017.

## Built on by (most cited first, 2 of 2)

- [SCAN: A Spatial Context Attentive Network for Joint Multi-Agent Intent Prediction](https://mlsanity.com/paper/2102.00109/) (2021)
- [Physically Aware 360^circ View Generation from a Single Image using Disentangled Scene Embeddings](https://mlsanity.com/paper/2512.10293/) (2025)

## Abstract (arXiv)

High level understanding of sequential visual input is important for safe and stable autonomy, especially in localization and object detection. While traditional object classification and tracking approaches are specifically designed to handle variations in rotation and scale, current state-of-the-art approaches based on deep learning achieve better performance. This paper focuses on developing a spatiotemporal model to handle videos containing moving objects with rotation and scale changes. Built on models that combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to classify sequential data, this work investigates the effectiveness of incorporating attention modules in the CNN stage for video classification. The superiority of the proposed spatiotemporal model is demonstrated on the Moving MNIST dataset augmented with rotation and scaling.