We present QueryInst, a new perspective for instance segmentation. QueryInst is a multi-stage end-to-end system that treats instances of interest as learnable queries, enabling query based object detectors, e.g., Sparse R-CNN, to have strong instance segmentation performance. The attributes of instances such as categories, bounding boxes, instance masks, and instance association embeddings are represented by queries in a unified manner. In QueryInst, a query is shared by both detection and segmentation via dynamic convolutions and driven by parallelly-supervised multi-stage learning. We conduct extensive experiments on three challenging benchmarks, i.e., COCO, CityScapes, and YouTube-VIS to evaluate the effectiveness of QueryInst in object detection, instance segmentation, and video instance segmentation tasks. For the first time, we demonstrate that a simple end-to-end query based framework can achieve the state-of-the-art performance in various instance-level recognition tasks.
Model | Backbone | Style | Lr schd | Number of Proposals | Multi-Scale | RandomCrop | box AP | mask AP | Config | Download |
---|---|---|---|---|---|---|---|---|---|---|
QueryInst | R-50-FPN | pytorch | 1x | 100 | False | False | 42.0 | 37.5 | config | model | log |
mindspore_gpu==2.2.0
Please run
python train.py
@InProceedings{Fang_2021_ICCV,
author = {Fang, Yuxin and Yang, Shusheng and Wang, Xinggang and Li, Yu and Fang, Chen and Shan, Ying and Feng, Bin and Liu, Wenyu},
title = {Instances As Queries},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {6910-6919}
}
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