Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Few-Shot Object Detection: A Survey   Humans are able to learn to recognize new objects even from a few examples.
In contrast, training deep-learning-based object detectors requires huge
amounts of annotated data. To avoid the need to acquire and annotate these huge
amounts of data, few-shot object detection aims to learn from few object
instances of new categories in the target domain. In this survey, we provide an
overview of the state of the art in few-shot object detection. We categorize
approaches according to their training scheme and architectural layout. For
each type of approaches, we describe the general realization as well as
concepts to improve the performance on novel categories. Whenever appropriate,
we give short takeaways regarding these concepts in order to highlight the best
ideas. Eventually, we introduce commonly used datasets and their evaluation
protocols and analyze reported benchmark results. As a result, we emphasize
common challenges in evaluation and identify the most promising current trends
in this emerging field of few-shot object detection.

3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga