# Target State Classification by Attention-Based Branch Expansion Network
## 2 Related Work
### Deep Learning Methods  
The philosophy behind deep learning is to mimic the way a child perceives the world from birth, learning to recognize objects as he/she grows up and processes large amounts of data [31]. An intuitive and convenient way is to use the advanced object detection network for image classification prediction, such as classical two-stage model Faster-RCNN and end-to-end model YOLOv4 [32]. However, fine-grained object-level annotations are required for state classification tasks. The application of deep learning in image classification tasks mainly includes the following models.  
In 2012, AlexNet [33] won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC); it has five convolutional layers followed by three fully connected (FC) layers with a dropout layer after each FC layer to reduce overfitting. This was also the first time that deep learning was used for large-scale image classification. After AlexNet, a series of CNN models emerged. VGG [34] was developed by the Visual Geometry Group of Oxford in 2014. It has two structures: VGG16 and VGG19 with 16 and 19 hidden layers, respectively. Compared with AlexNet, VGG uses several continuous stacked 3 \(\times\) 3 convolution kernels to replace the large convolution kernel in AlexNet (11 \(\times\) 11, 7 \(\times\) 7, 5 \(\times\) 5), because multiple nonlinear layers can increase the depth of the network to ensure learning more complex features. GoogleNet [35] is the 2014 ILSVRC champion model; a modular structure named Inception was proposed to maintain the sparsity of the neural network structure and make full use of the high computational performance of the dense matrix by clustering sparse matrix into more dense sub-matrices. Another special design is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Year** & **Performance** & **Accuracy on TrashNet** \\ \hline KNN & 1967 & Simple and efficient still used today & 88 [21] \\ SVM & 1995 & Represents a victory for Kernel technology & 80 [21] \\ RF & 2001 & The concept of integrated learning & 85 [21] \\ \hline AlexNet & 2012 & Champion in 2012 ILSVRC & 91 [21] \\ GoogleNet & 2014 & Champion in 2014 ILSVRC & 87.71 [22] \\ VGG-16 & 2014 & Runner-up in 2014 ILSVRC & 93 (fine-tuned) [21] \\ ResNet & 2015 & Champion in 2015 ILSVRC & 88.66 [22] \\ DenseNet & 2017 & CVPR’s Best Papers of 2017 & 89 [23] \\ Xception & 2017 & Great progress in GoogleNet series & 91.41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of related methods.  
to use the global average pooling (GAP) layer instead of the FC layer to reduce the number of parameters. However, with the deepening of the network, the gradient disappearance leads to degradation of network performance. The Resnet [16] proposes a residual structure that uses a shortcut connection to connect the input and output, which learns the residual to solve the degradation problem. ResNet won five champions at the ILSVRC & COCO 2015 Competitions. CNN showed performance improvements over SVM and RF [36], due to its ability to retrieve complex features and information from the input images on large sample datasets [30].  
A bad rule of thumb is that the deeper the network, the better the result, but channel width is important for certain recognition tasks [37]. The authors in [18] used Xception as the backbone. Xception is another structure proposed by GoogleNet based on Inception V3, decoupling channel correlation and spatial correlation, and deriving depthwise separable convolution to replace the original convolutional operation in Inception V3, which simplifies the model and improves the performance. Its architecture is very easy to define and modify, which meets the design requirements of our tasks. We use the core structure of Xception as the basis for a compromise between depth and width.