You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
In this paper, we apply the attention mechanism to autonomous driving for steering angle prediction. We propose the first model, applying the recently introduced sparse attention mechanism to visual domain, as well as the aggregated extension for this model. We show the improvement of the proposed method, comparing to no attention as well as to different types of attention.

Cited Abstract(s):
[1]: Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels. We present a novel recurrent neural network model that is capable of extracting information from an image or video by adaptively selecting a sequence of regions or locations and only processing the selected regions at high resolution. Like convolutional neural networks, the proposed model has a degree of translation invariance built-in, but the amount of computation it performs can be controlled independently of the input image size. While the model is non-differentiable, it can be trained using reinforcement learning methods to learn task-specific policies. We evaluate our model on several image classification tasks, where it significantly outperforms a convolutional neural network baseline on cluttered images, and on a dynamic visual control problem, where it learns to track a simple object without an explicit training signal for doing so.
One limitation of soft attention is that it only reweighs the convolutional features and therefore everything is always attended to, although not with the same relative importance (hence attention). In contrast, models only process part of the input, which is assumed to be the most important region. Because hard attention is not differentiable, it is more challenging to optimise. [1] proposes to learn hard attention from reinforcement learning. There are two crucial component in their network: The first one is a glimpse sensor, which can be used to extract a retina-like representation centred at a given location in the input; The second component is a glimpse network, it is used to process the retina-like representation extracted from the glimpse sensor, and the processed information is then fed into a recurrent neural network (RNN) which estimates the attention focus for the glimpse sensor at the next iteration.