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.
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. @cite_4 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.
@cite_4
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This paper presents experiments extending the work of (2014) on recurrent neural models for attention into less constrained visual environments, specifically fine-grained categorization on the Stanford Dogs data set. In this work we use an RNN of the same structure but substitute a more powerful visual network and perform large-scale pre-training of the visual network outside of the attention RNN. Most work in attention models to date focuses on tasks with toy or more constrained visual environments, whereas we present results for fine-grained categorization better than the state-of-the-art GoogLeNet classification model. We show that our model learns to direct high resolution attention to the most discriminative regions without any spatial supervision such as bounding boxes, and it is able to discriminate fine-grained dog breeds moderately well even when given only an initial low-resolution context image and narrow, inexpensive glimpses at faces and fur patterns. This and similar attention models have the major advantage of being trained end-to-end, as opposed to other current detection and recognition pipelines with hand-engineered components where information is lost. While our model is state-of-the-art, further work is needed to fully leverage the sequential input.