# Target State Classification by Attention-Based Branch Expansion Network
## 1 Introduction
Our datasets are low in the distinction between feature information; therefore, we try to explore  
Figure 1: Examples of the two datasets. The left sub-graph (**a**) is from “object-state” showing different tools for the same state (column) and the same tool for different states (row). The right sub-graph (**b**) is samples of different people in the same state (1 st row) and a person in different states ( 2 nd row) from the “wearing-state”. whether considering the depth and width of the network can improve the classification accuracy. We propose a novel attention-based branch expansion classification network with modified Xception as the backbone. The Xception architecture [18] is a linear stack of depthwise separable convolution layers, which makes the architecture very easy to define and modify. We modify and expand the middle structure of Xception and compare the network performance of different widths and depths to find the backbone structure that best represents the characteristics of our datasets. In addition, which backbone best fits the attention module applied in stacked block mode is explored. In addition, as can be seen from the samples in Figure 1, different states of the same object belong to different classes, and the different wear situations of the same person are different categories. Considering the particularity of the task of state classification and the challenge of the above-mentioned datasets, we need to explore more effective classification methods to extract activation features and suppress interference features. Therefore, we attach an attention mechanism to distinguish feature information effectively, strengthen key features and suppress confounding features. The attention mechanism used in computer vision is generally divided into spatial attention and channel attention. The former learns the appearance and location information and decides \"where\" to focus in the image, while the latter focuses on \"what\" is meaningful in a given image. Inspired by [19], we adopt the plug-and-play dual-attention module with negligible parameter overhead. The difference is that we modify the fusion of the two attention maps with a parallel residual connection, which can achieve a better effect on our datasets. The objective of this article is to solve the problem of the state classification of objects. Section 2 presents the related work of three categories of image classification. Section 3 describes the proposed method, and the appropriate depth and width of network structure. The application mode of the attention module is explored. Section 4 makes an extensive comparison between the eight models described in Section 3 and selects the most appropriate model structure. Section 5 discusses the correspondence between the experimental results and visualization conclusion, as well as the comparability with the SOTA methods. Section 6 summarizes the work of the article, reveals the shortcomings of the model, and gives possible future solutions. The contributions of our work are as follows:  
* We introduce two datasets, \"wearing-state\" and \"object-state\", the former with different staffs wearing different lab suits and the latter consisting of different states of different categories tools. The datasets are collected for technical state detection in the laboratory. * Three structures of Xception-based feature extraction backbones, with different stacked blocks, each consisting of parallel branches, are proposed to better express complex information. We propose four ABE networks based on the four backbones (including Xception) by adopting a plug-and-play dual-attention module with the parallel residual connection. * We conduct extensive ablation analysis on three datasets to explore the influence of depth and width on the classification performance, and prove the effectiveness of width broadening and a dual-attention mechanism through quantitative comparison and qualitative visualization. Our experiments show consistent improvements in classification performance on various datasets.