# RATM: Recurrent Attentive Tracking Model

arXiv: https://arxiv.org/abs/1510.08660 · HTML: https://mlsanity.com/paper/1510.08660/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2015.

## Built on by (most cited first, 4 of 4)

- [Learning What and Where: Disentangling Location and Identity Tracking Without Supervision](https://mlsanity.com/paper/2205.13349/) (2022)
- [MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration](https://mlsanity.com/paper/2204.08058/) (2022)
- [Recurrent Attention Models with Object-centric Capsule Representation for Multi-object Recognition](https://mlsanity.com/paper/2110.04954/) (2021)
- [SaccadeCam: Adaptive Visual Attention for Monocular Depth Sensing](https://mlsanity.com/paper/2103.12981/) (2021)

## Abstract (arXiv)

We present an attention-based modular neural framework for computer vision. The framework uses a soft attention mechanism allowing models to be trained with gradient descent. It consists of three modules: a recurrent attention module controlling where to look in an image or video frame, a feature-extraction module providing a representation of what is seen, and an objective module formalizing why the model learns its attentive behavior. The attention module allows the model to focus computation on task-related information in the input. We apply the framework to several object tracking tasks and explore various design choices. We experiment with three data sets, bouncing ball, moving digits and the real-world KTH data set. The proposed Recurrent Attentive Tracking Model performs well on all three tasks and can generalize to related but previously unseen sequences from a challenging tracking data set.