CenterInst: Center-Based Real-Time Instance Segmentation

Abstract

Instance segmentation is a computer vision task that aims to give each pixel in an image an instance-specific label. Recently, researchers have shown growing interest in real-time instance segmentation. In this paper, we propose a novel center-based real-time instance segmentation method (CenterInst), which follows the FastInst meta-architecture. Key design aspects include a center-guided query selector, a center-guided sampling-based query decoder, and a lightweight dual-path decoder. The center-guided query selector selects queries via the per-pixel prediction of center point probabilities, avoiding excessive query proposals for single instances. The center-guided sampling-based query decoder adaptively generates local sampling points based on center positions, employing adaptive mixing to update queries without irrelevant sampling disturbances. The lightweight dual-path decoder enhances inference speed and maintains accuracy via pixel decoding on every layer during training but only utilizing the final layer’s decoder during inference. The experimental results show CenterInst achieves superior accuracy and speed compared to state-of-the-art real-time instance segmentation methods.