# On Learning Where To Look

arXiv: https://arxiv.org/abs/1405.5488 · HTML: https://mlsanity.com/paper/1405.5488/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2014.

## Built on by (most cited first, 1 of 1)

- [Autonomous Curiosity for Real-Time Training Onboard Robotic Agents](https://mlsanity.com/paper/2109.00927/) (2021)

## Abstract (arXiv)

Current automatic vision systems face two major challenges: scalability and extreme variability of appearance. First, the computational time required to process an image typically scales linearly with the number of pixels in the image, therefore limiting the resolution of input images to thumbnail size. Second, variability in appearance and pose of the objects constitute a major hurdle for robust recognition and detection. In this work, we propose a model that makes baby steps towards addressing these challenges. We describe a learning based method that recognizes objects through a series of glimpses. This system performs an amount of computation that scales with the complexity of the input rather than its number of pixels. Moreover, the proposed method is potentially more robust to changes in appearance since its parameters are learned in a data driven manner. Preliminary experiments on a handwritten dataset of digits demonstrate the computational advantages of this approach.