Correspondence pruning aims to find correct matches (inliers) from an initial
set of putative correspondences, which is a fundamental task for many
applications. The process of finding is challenging, given the varying inlier
ratios between scenes/image pairs due to significant visual differences.
However, the performance of the existing methods is usually limited by the
problem of lacking visual cues (\eg texture, illumination, structure) of
scenes. In this paper, we propose a Visual-Spatial Fusion Transformer
(VSFormer) to identify inliers and recover camera poses accurately. Firstly, we
obtain highly abstract visual cues of a scene with the cross attention between
local features of two-view images. Then, we model these visual cues and
correspondences by a joint visual-spatial fusion module, simultaneously
embedding visual cues into correspondences for pruning. Additionally, to mine
the consistency of correspondences, we also design a novel module that combines
the KNN-based graph and the transformer, effectively capturing both local and
global contexts. Extensive experiments have demonstrated that the proposed
VSFormer outperforms state-of-the-art methods on outdoor and indoor benchmarks.

在本文中，我们提出了一种名为 VSFormer 的视觉空间融合转换器，通过交叉关注获取场景的高度抽象视觉线索，进而通过联合视觉空间融合模块将这些视觉线索嵌入到相应的对应关系中，实现对内点的识别和相机姿态恢复，同时通过结合基于 KNN 的图和转换器的新颖模块有效捕捉局部和全局背景的一致性，大量实验证明所提出的 VSFormer 在室内外基准上优于现有方法。

VSFormer：视觉空间融合 Transformer 用于对应关系修剪

VSFormer: Visual-Spatial Fusion Transformer for Correspondence Pruning

Embedding based methods are widely used for unsupervised keyphrase extraction
(UKE) tasks. Generally, these methods simply calculate similarities between
phrase embeddings and document embedding, which is insufficient to capture
different context for a more effective UKE model. In this paper, we propose a
novel method for UKE, where local and global contexts are jointly modeled. From
a global view, we calculate the similarity between a certain phrase and the
whole document in the vector space as transitional embedding based models do.
In terms of the local view, we first build a graph structure based on the
document where phrases are regarded as vertices and the edges are similarities
between vertices. Then, we proposed a new centrality computation method to
capture local salient information based on the graph structure. Finally, we
further combine the modeling of global and local context for ranking. We
evaluate our models on three public benchmarks (Inspec, DUC 2001, SemEval 2010)
and compare with existing state-of-the-art models. The results show that our
model outperforms most models while generalizing better on input documents with
different domains and length. Additional ablation study shows that both the
local and global information is crucial for unsupervised keyphrase extraction
tasks.

本文提出了一种新的方法来解决无监督关键词抽取任务，该方法联合建模本地和全局语境。本地语境是通过基于文档的图形结构计算的，而全局语境是通过矢量空间中的转型嵌入模型计算的。最后，联合建模全局和本地语境以进行排名。