## 1 Introduction  
Real-time hand segmentation information is essential in augmented reality (AR) applications involving human interaction with virtual objects. Additional instance-specific information can further enhance segmentation data to model complex interactions, but can be computationally expensive to obtain. AR has been proved to increase the effectiveness of motor function rehabilitation systems, particularly in upper extremities [1, 2]. In this study, we explore a light-weight semantic segmentation technique that preserves some instance information, aiming to power an AR-based upper-limb rehabilitation platform. This instance segmentation approximation allows minimizing the hardware requirements of the target rehabilitation platform, as this information is only occasionally required and is not critical for the proper functioning of the application. Instance segmentation is inherently a more challenging task than semantic segmentation, and is more closely related to object detection than semantic segmentation. Furthermore, in the general case, images can contain an arbitrary number of instances, requiring the use of clever segmentation strategies or complex architectures. Existing fully supervised instance segmentation methods can be divided into three categories based on number of stages present in the inference pipeline: single-stage, two-stage, and multi-stage methods. Two-stage instance segmentation is the most widely used [3], with Mask R-CNN the most representative work in this category [4]. These pipelinesgenerally compose object detection with semantic segmentation, and can be divided into top-down or bottom-up methods, depending on the order in which segmentation and detection operations are applied. Examples of top-down methods, which first select regions of interest and then segment, are Sharpmask [5], PANet [6], Mask SSD [7], Mask R-CNN [4], Mask Scoring R-CNN [8], MaskLab [9], and Explicit Shape Encoding (ESE) [10]. Examples of bottom-up methods, which first segment and then detect instances, are DeepCRFs [11], Dynamically instantiated Network (DIN) [12], Contour Information Deep Watershed Transform (DWT) [13], Pixel Center Prediction Box2 pix [14], Depth Information Pixel-level Encoding and Depth Layering (PEDL) [15], Recurrent Pixel Embedding (RPE) [16], Deep Metric Learning (DML) [17]. Example of multi-stage methods include those based on cascade architectures [18; 19; 20] and Recurrent Neural Network (RNN)-based approaches such as Recurrent Instance Segmentation (RIS) [21], End-to-End Instance Segmentation (ETE) [22], Recurrent Neural Networks for Semantic Instance Segmentation (RSIS) [23]. Additionally, transformers-based methods like QueryInst [24], SOLQ [25], ISTR [26], and Mask2 Former [27] exemplify advanced techniques in this domain. The use of multi-stage detectors presents a number of disadvantages. The overall performance is constrained by the weakest element of the pipeline, with errors cascading irreversibly through the inference process. Scalability may be compromised, as many multi-stage approaches require computation for each instance detected at some point in the pipeline. Some of these approaches, particularly top-down methods, do not segment images holistically, which reduces the contextual information available during segmentation. Certain detectors, moreover, cannot be trained end-to-end, and may require multiple datasets and complex training schemes. Single-stage detectors avoid these problems but require employing novel techniques to accomplish the complex task of instance segmentation. Some single-stage approaches use parallel branches, in favor of sequential ones, that perform subtasks that are composed into the final segmentation masks, such as You Only Look At Coefficients (YOLACT) [28] and SOLO [29]. Single-stage methods can be further divided into anchor-based and anchor-free methods. Examples of anchor-based methods are InstanceFCN [30], FCIS [31], TensorMask [32], and YOLACT [28], with the latter being the most representative.