Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MatchFormer: Interleaving Attention in Transformers for Feature Matching   Local feature matching is a computationally intensive task at the subpixel
level. While detector-based methods coupled with feature descriptors struggle
in low-texture scenes, CNN-based methods with a sequential extract-to-match
pipeline, fail to make use of the matching capacity of the encoder and tend to
overburden the decoder for matching. In contrast, we propose a novel
hierarchical extract-and-match transformer, termed as MatchFormer. Inside each
stage of the hierarchical encoder, we interleave self-attention for feature
extraction and cross-attention for feature matching, enabling a human-intuitive
extract-and-match scheme. Such a match-aware encoder releases the overloaded
decoder and makes the model highly efficient. Further, combining self- and
cross-attention on multi-scale features in a hierarchical architecture improves
matching robustness, particularly in low-texture indoor scenes or with less
outdoor training data. Thanks to such a strategy, MatchFormer is a multi-win
solution in efficiency, robustness, and precision. Compared to the previous
best method in indoor pose estimation, our lite MatchFormer has only 45%
GFLOPs, yet achieves a +1.3% precision gain and a 41% running speed boost. The
large MatchFormer reaches state-of-the-art on four different benchmarks,
including indoor pose estimation (ScanNet), outdoor pose estimation
(MegaDepth), homography estimation and image matching (HPatch), and visual
localization (InLoc). Code will be made publicly available at
https://github.com/jamycheung/MatchFormer.

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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-th
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The categories of this paper are: physics
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: plasm-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: solv-int
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The categories of this paper are: stat
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: bayes-an