# Recurrent Soft Attention Model for Common Object Recognition

## TL;DR

This paper introduces a Recurrent Soft Attention Model that enhances object recognition by integrating visual attention with LSTM memory, demonstrating improved accuracy on CIFAR-10.

## Contribution

The novel model combines recurrent attention with down-sampling and feedback mechanisms for better object recognition performance.

## Key findings

- Effective attention integration improves recognition accuracy.
- Down-sample network and feedback mechanism are crucial for performance.
- Achieved high top-1 accuracy on CIFAR-10.

## Abstract

We propose the Recurrent Soft Attention Model, which integrates the visual attention from the original image to a LSTM memory cell through a down-sample network. The model recurrently transmits visual attention to the memory cells for glimpse mask generation, which is a more natural way for attention integration and exploitation in general object detection and recognition problem. We test our model under the metric of the top-1 accuracy on the CIFAR-10 dataset. The experiment shows that our down-sample network and feedback mechanism plays an effective role among the whole network structure.