Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fine-Grained Image Analysis with Deep Learning: A Survey   Fine-grained image analysis (FGIA) is a longstanding and fundamental problem
in computer vision and pattern recognition, and underpins a diverse set of
real-world applications. The task of FGIA targets analyzing visual objects from
subordinate categories, e.g., species of birds or models of cars. The small
inter-class and large intra-class variation inherent to fine-grained image
analysis makes it a challenging problem. Capitalizing on advances in deep
learning, in recent years we have witnessed remarkable progress in deep
learning powered FGIA. In this paper we present a systematic survey of these
advances, where we attempt to re-define and broaden the field of FGIA by
consolidating two fundamental fine-grained research areas -- fine-grained image
recognition and fine-grained image retrieval. In addition, we also review other
key issues of FGIA, such as publicly available benchmark datasets and related
domain-specific applications. We conclude by highlighting several research
directions and open problems which need further exploration from the community.

36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph