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Few-shotObjectDetectionUsingOnlineRandomForests
WernerBailer,HannesFassold
JOANNEUMRESEARCH,DIGITAL–Institute for InformationandCommunicationTechnologies
{werner.bailer,hannes.fassold}@joanneum.at
Abstract. We propose an approach for few-shot ob-
jectdetection,consistingofaCNN-basedgenericob-
jectdetectorandfeatureextractor,andanonlineran-
dom forest as a classifier. This enables incremental
training of the classifier, which reaches similar per-
formancewitharound20samplesaswhenusing50+
training samples inbatch learning.
1. Introduction
In many practical applications for object detec-
tion, it is relevant to detect new classes or subclasses
of common objects, for which only very limited
training data are available. While a large amount
of literature on few-shot classification has been pub-
lished in recent years, the problem of few-shot de-
tection is more challenging, as it also involves iden-
tifying candidate regions for the yet unknown object
classes. The problem of few-shot detection can be
discriminated into the twofollowingcases.
Refinement of existing classes. The new class to
betrainedisaspecificsubclassofaclassalreadysup-
ported by an object detection algorithm, e.g., classi-
fying “truck”, when the classifier already has a class
“vehicle”. For thisapproach,anexistingdetectorand
classifier for the broader class (e.g. Yolo [6], Faster
R-CNN [7]) can be used, and an additional classifier
tobe trained/adapted for thenewclasses isneeded.
New classes. Candidate regions for such classes
willnotbefoundbythepretrainedclassifier, thusan-
otherdetectionapproach isneeded. Oneapproach to
find candidate regions is to use a detector trained on
“objectness”, i.e. the likelihood that a regions con-
tains a coherent object. On the identified candidate
regions feature extraction and classification can be
performed, similar to thefirst case. We aimto enable training newobject classes with
only few (i.e., 5-10) labeled examples, which may
also not be available all at once, but being added
gradually, improve the detector over time. The con-
tributionof thispaper is thususingaCNN-basedob-
jectdetectionframeworkforgenericobjectdetection
and feature extraction, and train an online classifier
on these features. After discussing related work in
Section 2, Section 3 presents the proposed approach
and results, andSection4concludes thepaper.
2.Relatedwork
[1] does not actually perform detection, but uses
bounding box regression as proposed in SSD to
improve the localisation of the region of interest.
Then binary object-or-not classification as proposed
in Faster R-CNN is used, and uses a modified Faster
R-CNN classifier to facilitate transfer learning. The
work proposes regularisation based on the probabil-
ity distribution of the known classes for the new tar-
get class. [2] propose a method for few-shot classifi-
cation and detection, bootstrapped from few labeled
instances. The method is based on components from
FasterRCNN,usingSelectiveSearchorEdgeBoxes
for region proposals, and iteratively adds bounding
box proposals and updates classifiers. [5] propose
a pipeline using faster R-CNN up to ROI pooling,
and two FC layers as feature extractors. Classifica-
tion is then performed using a kernel method. [4]
uses FPN to create an object detection pipeline us-
ing metric learning. Classification is done different
for pretrained classes (using Inception v3 [10] up
to FC2), while few-shot learning is done with FPN
(in the DCN variant) instead. [9] propose to train a
generic object detector on ImageNet, sampling pos-
itive and negative candidate regions. This approach
is suitable for generic object detection, beyond the
originally trained classes. An approach based on
meta-features and learning reweighting of those fea-
95
Joint Austrian Computer Vision and Robotics Workshop 2020
- Title
- Joint Austrian Computer Vision and Robotics Workshop 2020
- Editor
- Graz University of Technology
- Location
- Graz
- Date
- 2020
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-85125-752-6
- Size
- 21.0 x 29.7 cm
- Pages
- 188
- Categories
- Informatik
- Technik