# Multiple Object Recognition with Visual Attention

arXiv: https://arxiv.org/abs/1412.7755 · HTML: https://mlsanity.com/paper/1412.7755/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2014.

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

- [Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey](https://mlsanity.com/paper/2307.09615/) (2023)
- [Beyond Static Visual Tokens: Structured Sequential Visual Chain-of-Thought Reasoning](https://mlsanity.com/paper/2603.26737/) (2026)
- [Mind the GAP: Glimpse-based Active Perception improves generalization and sample efficiency of visual reasoning](https://mlsanity.com/paper/2409.20213/) (2024)
- [On Locality and Length Generalization in Visual Reasoning](https://mlsanity.com/paper/2607.09061/) (2026)

## Abstract (arXiv)

We present an attention-based model for recognizing multiple objects in images. The proposed model is a deep recurrent neural network trained with reinforcement learning to attend to the most relevant regions of the input image. We show that the model learns to both localize and recognize multiple objects despite being given only class labels during training. We evaluate the model on the challenging task of transcribing house number sequences from Google Street View images and show that it is both more accurate than the state-of-the-art convolutional networks and uses fewer parameters and less computation.