Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning for Fine-Grained Image Analysis: A Survey   Computer vision (CV) is the process of using machines to understand and
analyze imagery, which is an integral branch of artificial intelligence. Among
various research areas of CV, fine-grained image analysis (FGIA) is a
longstanding and fundamental problem, and has become ubiquitous in diverse
real-world applications. The task of FGIA targets analyzing visual objects from
subordinate categories, \eg, species of birds or models of cars. The small
inter-class variations and the large intra-class variations caused by the
fine-grained nature makes it a challenging problem. During the booming of deep
learning, recent years have witnessed remarkable progress of FGIA using deep
learning techniques. In this paper, we aim to give a survey on recent advances
of deep learning based FGIA techniques in a systematic way. Specifically, we
organize the existing studies of FGIA techniques into three major categories:
fine-grained image recognition, fine-grained image retrieval and fine-grained
image generation. In addition, we also cover some other important issues of
FGIA, such as publicly available benchmark datasets and its related domain
specific applications. Finally, we conclude this survey by highlighting several
directions and open problems which need be further explored by the community in
the future.

37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat