Papers › SOLQ: Segmenting Objects by Learning Queries
SOLQ: Segmenting Objects by Learning Queries
Bin Dong, Fangao Zeng, Tiancai Wang, Xiangyu Zhang, Yichen Wei
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Instance Segmentation | COCO test-dev | SOLQ (Swin-L, single scale) | mask AP | 46.7 | #35 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | SOLQ (ResNet101, single scale) | mask AP | 40.9 | #65 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | SOLQ (ResNet50, single scale) | mask AP | 39.7 | #76 of 112 | Archive leaderboard | report |
| Object Detection | COCO minival | SOLQ (Swin-L, single scale) | AP50 | 74.9 | #212 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | SOLQ (Swin-L, single scale) | AP75 | 61.3 | #212 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | SOLQ (Swin-L, single scale) | APL | 71.9 | #212 of 220 | Archive leaderboard | report |
| Object Detection | COCO test-dev | SOLQ (Swin-L, single scale) | AP50 | 74.6 | #41 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | SOLQ (Swin-L, single scale) | AP75 | 60.5 | #41 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | SOLQ (Swin-L, single scale) | APL | 70.6 | #41 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | SOLQ (Swin-L, single scale) | APM | 60 | #41 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | SOLQ (Swin-L, single scale) | APS | 37.6 | #41 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | SOLQ (Swin-L, single scale) | box mAP | 56.5 | #41 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | SOLQ (ResNet101, single scale) | box mAP | 48.7 | #103 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | SOLQ (ResNet50, single scale) | box mAP | 47.8 | #115 of 225 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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