# Retina-like Imaging and Its Applications: A Brief Review
## 3 Applications
### Deep Learning  
The retina-like mechanism has also been fully applied to the field of deep learning. When humans need to pay attention to a certain area, the light is concentrated to that area by turning their eyes to obtain a high-resolution image. Several works based on this mechanism have been used to solve various problems. [PERSON] et al. [57] proposed the human visual attention mechanism model, and thus, attention was introduced into the computer vision field. In 2017, [PERSON] et al. illustrated the fovea sampling grid of the biological primate retina through a deep learning computational model. This method is contrary to the previous methods and ideas of designing machine learning models based on biological results. The model is trained on the classification task and uses the least number of fixations in the visual scene under background disturbance. The tiling attributes appearing in the retina sampling grid of the trained model are obtained. They found that this lattice is similar to the primate retina. The eccentricity depends on the sampling lattice. The high-resolution area of the fovea is surrounded by the low-resolution periphery. Under certain conditions, these emerging characteristics are amplified or eliminated. Many state-of-the-art methods of attention-based classification are available for existing deep learning networks. However, these methods have a general limitation, that is, a large amount of data training is required to improve the accuracy of the model. [PERSON] et al. [58] proposed a guided attention recurrent network (GARN) based on retina-like scanning, as shown in Figure 10, to solve this problem. In this way, multiple ROIs are trained by scanning one image. The instructive ROI selection jointly determines the label category. This kind of guided multi-attention is trained with a small dataset to achieve high accuracy. Figure 9: Overview of the approach proposed by [PERSON] et al. Recurrent Neural Networks (RNNs): one for locating ROIs, and the other for classification. The different scales of the glimpse sensors are similar to the variable resolution characteristics of the human eye. Glimpse networks output features with variable resolution. And the guided attention is added to feature maps by Long Short-Term Memory (LSTM) networks. In 2020, [PERSON] et al. [59] proposed a novel peripheral fovea multi-resolution driving model based on the retina-like structure. This model predicts the speed of the car through the video of a dashcam. The overview of the peripheral fovea multi-resolution driving model is shown in Figure 11. The peripheral vision module processes complete video frames with low resolution, and the foveal vision module selects subregions and uses high-resolution input from these regions to improve its driving performances. They trained the fovea selection module under the supervision of the driver's line of sight. Adding high-resolution input from the predicted gaze position of a human driver will greatly improve the driving accuracy of the model. The multi-resolution fovea model is better than a single-resolution edge model with the same amount of floating-point operations. The driving model based on fovea multi-resolution achieves higher performance than conventional methods. The retina-like attention mechanism has also shown its unique advantages for practical uses in medical image processing. [PERSON] et al. [60] used the retina-like sequence scanning method to segment medical images. This method changes the position of the center point of the retina to perform higher-resolution processing on the subregions of the medical image that are difficult to classify. The accuracy of the overall segmentation is improved by increasing the segmentation accuracy of each subregion. Different from target tracking, the task of object detection and recognition is not only to find the target but also to classify it. The emergence of the retina-like mechanism makes the effect of object detection and recognition more remarkable.