Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Supervised Representation Learning on Document Images   This work analyses the impact of self-supervised pre-training on document
images in the context of document image classification. While previous
approaches explore the effect of self-supervision on natural images, we show
that patch-based pre-training performs poorly on document images because of
their different structural properties and poor intra-sample semantic
information. We propose two context-aware alternatives to improve performance
on the Tobacco-3482 image classification task. We also propose a novel method
for self-supervision, which makes use of the inherent multi-modality of
documents (image and text), which performs better than other popular
self-supervised methods, including supervised ImageNet pre-training, on
document image classification scenarios with a limited amount of data.

27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics