# CenterInst: Center-Based Real-Time Instance Segmentation
## 2 Related Work  
The existing instance segmentation methods can be grouped into three categories, i.e., region-, center-pixel-, and sparse-based methods.  
Region-based methods are traditional methods that first detect objects to determine the bounding box and then utilize fully convolutional networks [7] on candidate regions to perform segmentation. Mask R-CNN [8] is a classic region-based method that builds upon faster R-CNN [9] by adding a fully convolutional network for predicting segmentation results, enabling it to make predictions for classification, detection, and segmentation. Some methods [10; 11] improve upon mask R-CNN to obtain a more precise bounding box and segmentation mask. As the computational burden is heavy, it is difficult to establish them as real-time methods.  
Center-pixel-based methods assume that each pixel in the feature map can serve as the center of an instance and predict instance masks based on these centers. After mask prediction, NMS is applied for filtering. Typically, these methods are modifications of networks based on single-stage detectors like CenterNet, FCOS [12], etc. Among them, CondInst [13] decouples the task into mask kernel prediction and mask feature learning to generate object masks; YOLACT [14; 15] improves mask quality by cropping segmentation regions based on single-stage detection results; and SOLO eliminates the detection head, directly predicting the segmentation results from the centers, and uses segmentation NMS to filter duplicate results. Since these methods are primarily single-stage approaches, they are fast.  
These methods rely on many-to-one prediction and use NMS to eliminate redundancy. NMS has received much attention in real-time instance segmentation tasks. Based on the bipartite matching sample assignment strategy, initially widely applied to end-to-end object detection and subsequently to instance segmentation, sparse-based detection methods using bipartite matching do not require postprocessing with NMS to eliminate redundant predictions. K-Net [16] is based on sparse instance convolution kernels, and segmentation is achieved using the kernel update head. QueryInst [17] was designed based on a sparse detector (sparse R-CNN [18]). The results are generated by a convolution kernel produced by the query and the proposal extracted by sparse R-CNN. SparseInst predicts sparse instance activation maps to represent objects. SOLQ [19] learns mask embeddings for instance segmentation. In methods based on Transformer [18; 20] decoders, Mask2 Former leverages the Transformer structure to enhance its instance segmentation performance through masked attention. Mask DINO [21] and MP-Former [22] enhance the decoding capability of the decoder by using noisy labeled inputs, improving their overall performance. While these methods significantly reduce the number of predicted objects, it is difficult to improve the inference speed due to the complex decoder structures of some of these methods.  
The state-of-the-art real-time instance segmentation method, FastInst [4], is based on the Mask2 Former structure. It introduces instance activation to guide query generation and employs a dual-path decoding structure for query decoding, achieving improved speed and accuracy. Our CenterInst model builds upon FastInst, further enhancing its inference efficiency and improving accuracy.