  In this paper, we propose an end-to-end framework for instance segmentation.
Based on the recently introduced DETR [1], our method, termed SOLQ, segments
objects by learning unified queries. In SOLQ, each query represents one object
and has multiple representations: class, location and mask. The object queries
learned perform classification, box regression and mask encoding simultaneously
in an unified vector form. During training phase, the mask vectors encoded are
supervised by the compression coding of raw spatial masks. In inference time,
mask vectors produced can be directly transformed to spatial masks by the
inverse process of compression coding. Experimental results show that SOLQ can
achieve state-of-the-art performance, surpassing most of existing approaches.
Moreover, the joint learning of unified query representation can greatly
improve the detection performance of DETR. We hope our SOLQ can serve as a
strong baseline for the Transformer-based instance segmentation. Code is
available at https://github.com/megvii-research/SOLQ.
