# Dynamic Detection and Recognition of Objects Based on Sequential RGB Images
## 2 Related Works  
Instance segmentation is the most difficult task among the four classic visual tasks [16], which include classification, localization, detection, and segmentation. Its target is to obtain the pixel-level segmentation of individual objects, which combines the requirements of semantic segmentation and object detection. Over the past few years, deep learning has yielded a new generation of instance segmentation models with remarkable performance improvements and results in a paradigm shift in the field. There are two types of deep learning models for instance segmentation: two-stage models and one-stage models. The most important two-stage method is Mask-RNN [17] proposed by [PERSON] et al., which is extended from their earlier important work Faster RCNN [18]. Although two-stage methods have better performance, their computing burden is too heavy to run on some embedded devices. Thus, some researchers have proposed several excellent one-stage methods to reduce the computing burdens under the inspiration of the one-stage object detection frameworks [7; 8; 9; 10]. [PERSON] et al. [19] and [PERSON] et al. [20] designed special fully convolutional networks, together with positive-sensitive score maps, to segment instances. [PERSON] et al. proposed YOLACT [14] and improved it to YOLACT++ [21], which achieved the best balance between speed and accuracy. [PERSON] et al. [22] used a polar mask to annotate the segmentation of an instance, which is a more precise bounding box. CenterMask [23; 24] was developed from CenterNet [10] by inheriting the anchor-free ideas. [PERSON] et al. [25] represented the mask into a two-dimensional vector, which can be combined with the box detection branch. BlenderMask [26] combines the top-down and bottom-up methods based on an anchor-free framework. Zero-shot learning (ZSL), which is one of the typical transfer learning methods, is perhaps the supreme goal of machine learning. For example, if machines could classify new classes accurately [27; 28], we could collect labeled data as much as possible for free; if machines could reject samples of unknown classes [29; 30], any recognition system would be shielded against outliers. Specifically, the goal of ZSL is to recognize objects of unseen classes, whose labels are not available, by learning high-level semantic information [26; 27; 30]. In the pre-deep-learning era, researchers focused on the conventional or standard ZSL, in which all test images come from the unseen classes only. Various semantic embedding methods have been developed based on traditional machine learning technologies [27; 31; 32]. A semantic embedding method learns to embed the original features into a new semantic descriptor space and then predict the classification of features via matching the most similar semantic descriptor. In the past five years, the more challenging generalized zero-shot learning [3] (GZSL), in which the test set consists of data from both the seen and unseen classes and semantic embedding performs poorly, has attracted increasing attention. In a GZSL task, the training set only contains annotated objects of seen classes, but the test set contains objects from both seen and unseen classes. The extreme data imbalance of GZSL makes semantic embedding methods apt to be highly overfitted to seen classes and fail to classify the unseen classes [27]. Recently, some feature generation methods have been proposed to compensate for the lack of training images of unseen classes in GZSL. [PERSON] et al. [33] generated features of unseen classes with four different generative models, including generative moment matching network, auxiliary classifier GANs, denoising autoencoder, and adversarial autoencoder. The f-CLSWGAN has also been utilized to generate the unseen features conditioned on the class-level semantic descriptors [34]. Some methods [35; 36] introduced reverse regressor networks into the generator network in the form of a cycle-consistent loss or to constrain the feature [37]. [PERSON] et al.