In recent years, video semantic segmentation has made great progress with
advanced deep neural networks. However, there still exist two main challenges
\ie, information inconsistency and computation cost. To deal with the two
difficulties, we propose a novel motion-state alignment framework for video
semantic segmentation to keep both motion and state consistency. In the
framework, we first construct a motion alignment branch armed with an efficient
decoupled transformer to capture dynamic semantics, guaranteeing region-level
temporal consistency. Then, a state alignment branch composed of a stage
transformer is designed to enrich feature spaces for the current frame to
extract static semantics and achieve pixel-level state consistency. Next, by a
semantic assignment mechanism, the region descriptor of each semantic category
is gained from dynamic semantics and linked with pixel descriptors from static
semantics. Benefiting from the alignment of these two kinds of effective
information, the proposed method picks up dynamic and static semantics in a
targeted way, so that video semantic regions are consistently segmented to
obtain precise locations with low computational complexity. Extensive
experiments on Cityscapes and CamVid datasets show that the proposed approach
outperforms state-of-the-art methods and validates the effectiveness of the
motion-state alignment framework.

本研究提出了一种基于运动状态对齐的视频语义分割方法，该方法解决了信息不一致性和计算成本等难点，实现了像素级状态一致性和区域级时间一致性，能够以低计算成本高精度地分割出视频语义区域，并在 Cityscapes 和 CamVid 数据集上验证了其优越性。

视频语义分割的运动状态对齐

Motion-state Alignment for Video Semantic Segmentation

Retrieval based open-domain QA systems use retrieved documents and
answer-span selection over retrieved documents to find best-answer candidates.
We hypothesize that multilingual Question Answering (QA) systems are prone to
information inconsistency when it comes to documents written in different
languages, because these documents tend to provide a model with varying
information about the same topic. To understand the effects of the biased
availability of information and cultural influence, we analyze the behavior of
multilingual open-domain question answering models with a focus on retrieval
bias. We analyze if different retriever models present different passages given
the same question in different languages on TyDi QA and XOR-TyDi QA, two
multilingualQA datasets. We speculate that the content differences in documents
across languages might reflect cultural divergences and/or social biases.

本研究针对基于检索的开放领域问答系统，探究多语言环境下文献信息不一致的问题，分析多语言开放领域问答模型的检索偏差，探讨文化差异和信息不一致性对模型的影响。