In this paper, we aim to study how to build a strong instance segmenter with
minimal training time and GPUs, as opposed to the majority of current
approaches that pursue more accurate instance segmenter by building more
advanced frameworks at the cost of longer training time and higher GPU
requirements. To achieve this, we introduce a simple and general framework,
termed Mask Frozen-DETR, which can convert any existing DETR-based object
detection model into a powerful instance segmentation model. Our method only
requires training an additional lightweight mask network that predicts instance
masks within the bounding boxes given by a frozen DETR-based object detector.
Remarkably, our method outperforms the state-of-the-art instance segmentation
method Mask DINO in terms of performance on the COCO test-dev split (55.3% vs.
54.7%) while being over 10X times faster to train. Furthermore, all of our
experiments can be trained using only one Tesla V100 GPU with 16 GB of memory,
demonstrating the significant efficiency of our proposed framework.

建立一个强大的实例分割器，通过引入一个简单通用的框架 Mask Frozen-DETR，可以将任何现有的 DETR-based 目标检测模型转换为一个强大的实例分割模型，从而在性能和训练效率方面超过了最先进的实例分割方法 Mask DINO。