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[ICCV 2021] Instances as Queries
| Date | Stars |
|---|---|
| 2026-07-24 | 415 |
| 2026-07-25 | 415 |
| 2026-07-28 | 415 |
| 2026-07-30 | 415 |
| 2026-08-06 | 415 |
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<div align="center"> # Instances as Queries </div> <!-- [](https://paperswithcode.com/sota/instance-segmentation-on-coco-minival?p=queryinst-parallelly-supervised-mask-query) [](https://paperswithcode.com/sota/instance-segmentation-on-coco?p=queryinst-parallelly-supervised-mask-query) [](https://paperswithcode.com/sota/object-detection-on-coco-minival?p=queryinst-parallelly-supervised-mask-query) [](https://paperswithcode.com/sota/object-detection-on-coco?p=queryinst-parallelly-supervised-mask-query) --> <div align="center"> <img width="100%" alt="QueryInst-VIS Demo" src="https://user-images.githubusercontent.com/45201863/120617230-7d34a600-c48c-11eb-8a43-d61689a050be.gif"> </div> * **[News]** * **`Apr, 2022`:** If you like `QueryInst` for instance segmentation, you might also like `TeViT` (CVPR 2022, oral, [paper](https://arxiv.org/abs/2204.08412) / [code & models](https://github.com/hustvl/TeViT)) for high-performance video instance segmentation!. * **`Oct, 2021`:** `QueryInst (ICCV 2021)` is now officially included by `mmdetection` library, with new checkpoints, corresponding logs, and augmented training settings. We suggest you to use the newest `QueryInst` implementation in `mmdetection`, meanwhile this repo will be maintained too. Issues are welcomed if you have problems using `QueryInst` to reproduce the COCO AP reported in our paper. * **TL;DR:** **QueryInst (Instances as Queries)** is a simple and effective query based instance segmentation method driven by parallel supervision on dynamic mask heads, which outperforms previous arts in terms of both accuracy and speed. * Our QueryTrack (_i.e., Tracking Instances as Queries,_ [tech report](https://arxiv.org/abs/2106.11963)) based on QueryInst won [**the 2nd place** `(AP = 52.3 @ test set, AP = 54.3 @ val set)`](https://competitions.codalab.org/competitions/28988#results) in video instance segmentation (VIS) track with **single online end-to-end model, single scale testing & without using extra video training data** in the [3rd Large-scale Video Object Segmentation Challenge, CVPR 2021](https://youtube-vos.org/challenge/2021/). * For the first time, we demonstrate that an end-to-end query based framework driven by parallel supervision is competitive with well-established and highly-optimized methods in a wide range of instance-level recognition tasks ([object detection](https://paperswithcode.com/sota/object-detection-on-coco), [instance segmentation](https://paperswithcode.com/sota/instance-segmentation-on-coco) and video instance segmentation). # > [**Instances as Queries**](https://openaccess.thecvf.com/content/ICCV2021/papers/Fang_Instances_As_Queries_ICCV_2021_paper.pdf) > > by [Yuxin Fang\*](https://scholar.google.com/citations?user=_Lk0-fQAAAAJ&hl=en), [Shusheng Yang\*](https://scholar.google.com/citations?hl=zh-CN&user=v6dmW5cntoMC&view_op=list_works&citft=1&email_for_op=2yuxinfang%40gmail.com&gmla=AJsN-F53CnxYBtSUBs91e_N7uL7139t5ufTWFZ-r8k5oNe1haqf_6f8AE0uyoqnVBPqNG8MGOPH_ep6k_-gMW9KmflOUalJPYu1VTaE2IVjNVn1k-lDjzMEN_oN_a7MySKPieyFEPwMfabczLcR4Qg14seBM3mx6QXUu9Hj5QMZrg9jbKDOGQxxeVX0DJtjiWCGr2ukQgSIR4VVetSaGei48SNUkO8zol-8hApyNYZcUBLD6n9FvTEeE94iLiIbFbNP0m59fh3_z), [Xinggang Wang†](https://xwcv.github.io/), [Yu Li](http://yu-li.github.io), [Chen Fang](https://scholar.google.com/citations?hl=en&user=Vu1OqIsAAAAJ&view_op=list_works&citft
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:df9e9bf565d6d63f, topic:computer-vision, readme:object detection, readme:instance segmentation