Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Paying Attention to Multiscale Feature Maps in Multimodal Image Matching   We propose an attention-based approach for multimodal image patch matching
using a Transformer encoder attending to the feature maps of a multiscale
Siamese CNN. Our encoder is shown to efficiently aggregate multiscale image
embeddings while emphasizing task-specific appearance-invariant image cues. We
also introduce an attention-residual architecture, using a residual connection
bypassing the encoder. This additional learning signal facilitates end-to-end
training from scratch. Our approach is experimentally shown to achieve new
state-of-the-art accuracy on both multimodal and single modality benchmarks,
illustrating its general applicability. To the best of our knowledge, this is
the first successful implementation of the Transformer encoder architecture to
the multimodal image patch matching task.

16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph