令和3年度 電気・電子・情報関係学会 四国支部連合大会 講演論文集
2021 Shikoku-Section Joint Convention Record of the Institutes of Electrical and Related Engineers

1-7
Chirality Created by Semi-Supervised Object Detection
with Consistency Loss
Yuji YAMAUCHI

Yoko UWATE and Yoshifumi NISHIO( Tokushima University)

Introduction

Chirality created in the process of the model

Large datasets are essential to successful object detection. In addition, labeling objects can be very costly, time
consuming, and labor intensive. For example, Caltech’spedestrian detection benchmark took about 400 hours toannotate 250,000 images. Semi-supervised learning uses unlabeled data in combination with box-level labeled data toreduce the cost of labeling. Among them, we are focusing
on the method using consistency loss. In the model using
consistency loss, a pair of unlabeled data and data that is
flipped horizontally is used as input, and the diﬀerence between outputs is defined as consistency loss. Furthermore,
it was found that visual cirality also occurs in the data expansion that has been used as a matter of course in orderto prepare the large-scale data set required for object detection.
Therefore, we considered that the image with perturbation added in semi-supervised learning would have chirality,
which would aﬀect the accuracy of detection. We aim tocreate a semi-supervised object detection model that shows
the latest accuracy, and in this study, as a first step, wewill investigate the nature of semi-supervised learning using consistency loss.

Mathod. We investigate whether chirality can be seen in
the inverted image generated in the process of the semisupervised learning model using the above-mentioned consistency loss. Detection models trained with VOC07 or
VOC07 (no-text) which leaves only the image without text,
detects the inverted image in the test data.
Results. Table 2 shows the result of the detection model
detecting an image in which an object with chirality is reflected. This result indicates that the inverted image generated in the process of the semi-supervised learning modelthat takes advantage of the inconsistency may have chirality. Figure 1 shows what part the detection model is looking
at when it detects the inverted image. The red part shows
the chirality more strongly. In Fig.1, you can see that theletters show strong chirality.

Table 2: Inverted image detection accuracy.
Training data
VOC07
VOC07(no text)

Test accuracy (%)
81.2
73.5

Eﬀect of perturbation on detection accuracy
Mathod. We compared and evaluated the detection accuracy due to the diﬀerence in perturbations applied tothe input image during the training process in the semisupervised learning using consistency loss. Supervised
learning with labeled data (VOC07) only, semi-supervised
learning with VOC07 and horizontally inverted unlabeleddata (VOC12), semi-supervised learning with VOC07 andupside down VOC12, each learning The models that havebeen made are compared.

(a)OriginalFigure 1: Image with chirality-revealing regions highlighted.

Results. Table 1 shows the detection accuracy by themodel. From this result, it was found that the detection
accuracy is aﬀected by the diﬀerence in perturbation applied to the input image. We considered that one of thereasons for this result was that chirality influences detection accuracy.

Conclusion
In this research, the visual chirality that occurs in image inversion, which has been used in data expansion as amatter of course, is also applied to the perturbations added
to images in semi-supervised learning, which may aﬀect thedetection accuracy. We wondered if this was the case andinvestigated the nature of semi-supervised learning using
coherence loss.
The results showed that the diﬀerence in perturbations
applied to the input image in the model aﬀects the detection accuracy and that the inverted image generated in theprocess shows chirality.

Table 1: Detection accuracy.
Labeled data

Unlabeled datamAP

VOC07
VOC07
VOC07VOC12(Horizontal)
VOC12(Vertical)

70.3
72.6
72.1

(b)Flipped–7–