Recently, query based object detection frameworks achieve comparable
performance with previous state-of-the-art object detectors. However, how to
fully leverage such frameworks to perform instance segmentation remains an open
problem. In this paper, we present QueryInst (Instances as Queries), a query
based instance segmentation method driven by parallel supervision on dynamic
mask heads. The key insight of QueryInst is to leverage the intrinsic
one-to-one correspondence in object queries across different stages, as well as
one-to-one correspondence between mask RoI features and object queries in the
same stage. This approach eliminates the explicit multi-stage mask head
connection and the proposal distribution inconsistency issues inherent in
non-query based multi-stage instance segmentation methods. We conduct extensive
experiments on three challenging benchmarks, i.e., COCO, CityScapes, and
YouTube-VIS to evaluate the effectiveness of QueryInst in instance segmentation
and video instance segmentation (VIS) task. Specifically, using ResNet-101-FPN
backbone, QueryInst obtains 48.1 box AP and 42.8 mask AP on COCO test-dev,
which is 2 points higher than HTC in terms of both box AP and mask AP, while
runs 2.4 times faster. For video instance segmentation, QueryInst achieves the
best performance among all online VIS approaches and strikes a decent
speed-accuracy trade-off. Code is available at
https://github.com/hustvl/QueryInst.

提出一种 QueryInst 方法，以查询为基础进行实例分割，其实现是通过在动态掩模头上进行并行监督。经过各种实验后，QueryInst 在实例分割和视频实例分割（VIS）任务上达到了最佳表现，在所有在线 VIS 方法中取得了最佳性能并达到适当的速度 - 准确度平衡。

实例作为查询

Instances as Queries

Neural machine translation~(NMT) is ineffective for zero-resource languages.
Recent works exploring the possibility of unsupervised neural machine
translation (UNMT) with only monolingual data can achieve promising results.
However, there are still big gaps between UNMT and NMT with parallel
supervision. In this work, we introduce a multilingual unsupervised NMT
(\method) framework to leverage weakly supervised signals from high-resource
language pairs to zero-resource translation directions. More specifically, for
unsupervised language pairs \texttt{En-De}, we can make full use of the
information from parallel dataset \texttt{En-Fr} to jointly train the
unsupervised translation directions all in one model. \method is based on
multilingual models which require no changes to the standard unsupervised NMT.
Empirical results demonstrate that \method significantly improves the
translation quality by more than 3 BLEU score on six benchmark unsupervised
translation directions.

本研究提出了一种跨语言无监督神经机器翻译框架，利用来自高资源语言对的弱监督信号，以提高零资源翻译质量。该框架基于多语言模型，不需要对标准无监督神经机器翻译进行改动，实验结果表明使用该框架可以在六个基准无监督翻译方向上将翻译质量提高超过 3 个 BLEU 分数。