## 2 Related Work  
There are a number of approaches; for instance, segmentation. Mask R-CNN (Region-CNN) [6] increased a branch on the basis of Faster R-CNN [22], which can detect and segment the instance objects efficiently. Succeeding Mask R-CNN, [PERSON] et al. [7] proposed an end-to-end instance segmentation method based on the fully convolutional network by introducing position-sensitive internal and external score maps. To solve the problem of instance segmentation, sequential grouping networks (SGN) [23] gradually constructed object instance mask through a series of sub-grouping networks, and can tackled the problem of object occlusion faced by instance segmentation. To enhance information flow in proposal-based framework, [PERSON] et al. extended Mask R-CNN by adding a bottom-up path augmentation and presents adaptive feature pooling to avoid arbitrary allocation of proposal [14]. In order to deal with the instance-aware features and semantic segmentation labels simultaneously, single-shot instance segmentation with affinity pyramid networks (SSAP) [24] was proposed, which was a proposal-free instance segmentation method and calculated the probability that two pixels pertained to the same object in a hierarchical way according to a pixel-pair affinity pyramid. However, the real-time problem of instance segmentation was not completely solved. [PERSON] et al. [12] decomposed the instance segmentation problem into two parallel subtasks to improve real-time performance, and combined the prototype masks with the mask coefficients produced by the two subtasks linearly to generate the final result. After that, [PERSON] et al. allocated categories to per-pixel within an instance based on the location and size of the instance, and transforms instance segmentation into a solvable single classification problem [25]. Besides, [PERSON] et al. [26] presented two valid methods to cope with high-quality center samples and optimize the dense distance regression, respectively, which can obviously enhance the performance of instance segmentation and simplify the inference process. In addition, SOLOv2 [27] also followed the idea of segmenting objects by location (SOLO) [25] to learn the mask head of the instance segmenter dynamically to develop masks with higher accuracy. Multi-level feature networks are widely used in instance segmentation tasks to improve the performance of algorithms [28]. The low-level features in instance segmentation networks obtain high resolution and abundant detail information but lack semantic information. Moreover, the high-level features contain abundant semantic information, but the resolution is low and the perception of details is weak. Therefore, the appropriate fusion of low-level features and high-level features can improve the network performance. Fully convolutional networks (FCN) [29] merged semantic features from deep and coarse layer with appearance features from shallow and fine layer through skip-connections to segment accurately and in detail. Correspondingly, Inside-outside net (ION) [30] adopted skip pooling to connect the feature maps of different convolutional layers to realize multi-level feature fusion. Subsequently, [PERSON] et al. [31] combined high-level features with low-level features by a contraction path for capturing context and a symmetric extension path for precise positioning. Inspired by the human visual pathway, Top-down modulation (TDM) networks [32] utilized a top-down modulation network to supplement the standard bottom-up feedforward network, which is accomplished by lateral connections. Similarly, [PERSON] [15] exploited the inherent multi-scale and pyramid hierarchy of convolutional networks to build feature pyramids, and proposes a top-down structure with lateral connections to construct high-level semantic feature maps. Besides, single shot multibox detector (SSD) [33] integrated the predictions of multiple feature maps from different resolutions to deal with objects of various sizes naturally.