  Recent attention in instance segmentation has focused on query-based models.
Despite being non-maximum suppression (NMS)-free and end-to-end, the
superiority of these models on high-accuracy real-time benchmarks has not been
well demonstrated. In this paper, we show the strong potential of query-based
models on efficient instance segmentation algorithm designs. We present
FastInst, a simple, effective query-based framework for real-time instance
segmentation. FastInst can execute at a real-time speed (i.e., 32.5 FPS) while
yielding an AP of more than 40 (i.e., 40.5 AP) on COCO test-dev without bells
and whistles. Specifically, FastInst follows the meta-architecture of recently
introduced Mask2Former. Its key designs include instance activation-guided
queries, dual-path update strategy, and ground truth mask-guided learning,
which enable us to use lighter pixel decoders, fewer Transformer decoder
layers, while achieving better performance. The experiments show that FastInst
outperforms most state-of-the-art real-time counterparts, including strong
fully convolutional baselines, in both speed and accuracy. Code can be found at
https://github.com/junjiehe96/FastInst .
