SOLQ: Segmenting Objects by Learning Queries

Autor:	Dong, Bin, Zeng, Fangao, Wang, Tiancai, Zhang, Xiangyu, Wei, Yichen
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by NeurlPS 2021. Code is available at https://github.com/megvii-research/SOLQ
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2106.02351 Zobrazit plný text záznamu View this record from Arxiv