# Instance Segmentation Method of User Interface Component of Games
## 2 Previous Works
### Segmentation Methods  
Instance segmentation is a crucial, complex, and challenging area in research on machine vision. With the aim of predicting object class labels and object instance masks for each pixel, it localizes various object instance classes that are present in various images. With the advent of convolutional neural networks (CNN), various instance segmentation frameworks have been proposed [22; 23; 24]. They rapidly increased the segmentation accuracy [25]. Mask R-CNN [22] is a simple and efficient instance segmentation approach. Fully convolutional networks (FCN) that took the initiative in Fast/Faster R-CNN [26; 27] were used to predict segmentation masks along with box regression and object classification. For high performance, a feature pyramid network (FPN) [28] was used to extract step-by-step network functions and has a semantically powerful function using a top-down network path having lateral connections. The instance segmentation can be largely divided into the following four methods: (1) classification of mask proposals, (2) detection followed by segmentation, (3) labeling pixels followed by clustering, and (4) dense sliding window methods. Classification of mask proposals was gradually transformed from the classical method to a new method with an efficient structure such as RCNN [29]. Despite the better segmentation accuracy, there were some issues with RCNN-related technologies. For example, it is based on a multistage pipeline that is slow and hard to optimize since each stage must be learned separately. As the functions for each proposal should be extracted from the entire image of CNN, issues arose such as storing, time, and scale of detection. Later on, RCNN was followed by Fast RCNN [26] and Faster RCNN [27], which solved these issues. One of the most successful technologies related to detection followed by segmentation is Mask RCNN [22]. Mask RCNN extends Faster R-CNN [27] detection algorithm by employing a relatively simple mask predictor. Mask RCNN is simple to train, better generalized, and can be implemented by adding a little computational overhead to Faster R-CNN. An instance segmentation approach based on Mask R-CNN [30; 31; 32] has recently shown promising results in the instance segmentation problem. Another approach to instance segmentation, labeling pixels followed by clustering [24; 33; 34], uses the technology generated for semantic segmentation [35; 36]. This approach includes labeling by category of all image pixels. The pixels are then grouped into object instances using a clustering algorithm. This approach benefits from the positive development of modern semantic segmentation that can predict high-resolution object masks. Compared to detection followed by segmentation, labeling pixels followed by clustering is less accurate in frequently used benchmarks. Generally, more computational power is required because of the powerful computation capability required for pixel labeling. DeepMask [37; 38], InstanceFCN [39], etc., use CNN to generate mask proposal by dense sliding window methods. TensorMask [40] improves performance by using a different architecture compared to class agnostic mask generation techniques. In addition, unlike DeepMask and InstanceFCN, TensorMask includes classification for several classes performed in parallel with the prediction mask. This function is effective in instance segmentation. Especially, the segmentation method in the face recognition had a great influence on the face recognition and generation method. DeepLabv3+, a model proposed in [41], improved segmentation results along object boundaries by extending DeepLabv3 through adding a simple and effective decoder module. In addition, they produced a faster and more powerful encoder-decoder network by further exploring the Xception model and applying separable convolution by depth to both Atrous Spatial Pyramid Pooling and decoder modules. In International Conference on Multimedia Retrieval (ICMR) in 2015, [PERSON]. [42] published a face detector based on the basic AlexNet.