# Attend Before you Act: Leveraging human visual attention for continual learning

arXiv: https://arxiv.org/abs/1807.09664 · HTML: https://mlsanity.com/paper/1807.09664/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2018.

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

- [Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks](https://mlsanity.com/paper/2107.06405/) (2021)
- [Towards self-attention based visual navigation in the real world](https://mlsanity.com/paper/2209.07043/) (2022)

## Abstract (arXiv)

When humans perform a task, such as playing a game, they selectively pay attention to certain parts of the visual input, gathering relevant information and sequentially combining it to build a representation from the sensory data. In this work, we explore leveraging where humans look in an image as an implicit indication of what is salient for decision making. We build on top of the UNREAL architecture in DeepMind Lab's 3D navigation maze environment. We train the agent both with original images and foveated images, which were generated by overlaying the original images with saliency maps generated using a real-time spectral residual technique. We investigate the effectiveness of this approach in transfer learning by measuring performance in the context of noise in the environment.