我们发现Mask2Former还可以在视频实例分段上实现最先进的性能，而无需修改架构，丢失甚至培训管道。在本报告中，我们通过直接预测3D分段卷来显示通用图像分割体系结构通过直接预测3D分段卷来概括到视频分段。具体而言，Mask2Former在Youtubevis-2021上为Youtubevis-2019和52.6 AP设置了新的60.4 AP最先进的。鉴于其在图像分割中的多功能性，我们认为蒙版2格相符也能够处理视频语义和Panoptic分割。我们希望这将使最先进的视频分段研究更可访问，并更加关注设计通用图像和视频分段架构。

The standard closed-set domain adaptation approaches seek to mitigate distribution discrepancies between two domains under the constraint of both sharing identical label sets. However, in realistic scenarios, finding an optimal source domain with identical label space is a challenging task. Partial domain adaptation alleviates this problem of procuring a labeled dataset with identical label space assumptions and addresses a more practical scenario where the source label set subsumes the target label set. This, however, presents a few additional obstacles during adaptation. Samples with categories private to the source domain thwart relevant knowledge transfer and degrade model performance. In this work, we try to address these issues by coupling variational information and adversarial learning with a pseudo-labeling technique to enforce class distribution alignment and minimize the transfer of superfluous information from the source samples. The experimental findings in numerous cross-domain classification tasks demonstrate that the proposed technique delivers superior and comparable accuracy to existing methods.

与标准闭合域的适应任务相反，部分域适应设置通过放松相同的标签集假设来迎合现实情况。但是，源标签集集成了目标标签集的事实，因此引入了一些额外的障碍，因为私人源类别样本的培训阻止了相关的知识转移并误导了分类过程。为了减轻这些问题，我们设计了一种机制，用于策略选择高度自信的目标样本，这对于估算班级的体重所必需的必不可少的机制。此外，我们通过将实现紧凑型和不同类别分布的过程与对抗性目标结合过程来捕获类歧视和域的不变特征。对众多跨域分类任务的实验发现证明了所提出的技术具有比现有方法具有卓越和可比精度的潜力。