Semantic Segmentation by Semi-Supervised

Learning Using Time Series Constraint

Takahiro Mano

, Sota Kato

and Kazuhiro Hotta

Meijo University, 1-501 Shiogamaguchi, Tempaku-ku, Nagoya 468-8502, Japan

Keywords:

Semantic Segmentation, Semi-Supervised Learning, Pseudo Label, Time Series Constraint.

Abstract:

In this paper, we propose a method to improve the accuracy of semantic segmentation when the number of

training data is limited. When time-series information such as video is available, it is expected that images

that are close in time-series are similar to each other, and pseudo-labels can be easily assigned to those images

with high accuracy. In other words, if the pseudo-labels are assigned to the images in the order of time-series,

it is possible to efﬁciently collect pseudo-labels with high accuracy. As a result, the segmentation accuracy can

be improved even when the number of training images is limited. In this paper, we evaluated our method on

the CamVid dataset to conﬁrm the effectiveness of the proposed method. We conﬁrmed that the segmentation

accuracy of the proposed method is much improved in comparison with the baseline without pseudo-labels.

1 INTRODUCTION

Semantic segmentation is the process of assigning a

label to each pixel in an image. In general, image

recognition using deep learning requires a large num-

ber of supervised images. Semantic segmentation re-

quires pixel-level annotation in all images, and the

cost of preparing a large number of supervised im-

ages is very high. For example, it is reported that it

takes approximately 90 minutes to create one anno-

tated image in the Cityscapes dataset (Cordts et al.,

2016).

FCN (Long et al., 2015) which consists of all-

layer convolution, SegNet (Badrinarayanan et al.,

2017) and U-Net (Ronneberger et al., 2015) with

encoder-decoder structures, and Deeplabv3+ (Chen

et al., 2018) which is the extension of these meth-

ods have been proposed. But, they suffer from low

accuracy when those methods are trained with only

a small number of supervised images. Against this

background, semi-supervised learning method using a

small number of supervised and lots of unsupervised

images has attracted attention. (Papandreou et al.,

2015)

Recently, semi-supervised image segmentation

(SSIS) methods (Chen et al., 2021; Ouali et al., 2020)

has been proposed to train models with a limited num-

https://orcid.org/0000-0003-2077-6079

https://orcid.org/0000-0003-0392-6426

https://orcid.org/0000-0002-5675-8713

ber of labeled and unlabeled images. However, ex-

isting SSIS methods do not use a large number of

unlabeled images and fail to take advantage of unla-

beled images. The method (Chen et al., 2020) used

additional teacher signals such as pseudo-labels for

unlabeled images in order to improve the accuracy.

However, these methods are not designed for video

data and do not take into account the good property

of time-series data, such that the images at time t and

t + 1 are similar to each other. We consider that time

series constraints should be useful to improve the ac-

curacy.

In this paper, we propose to a segmentation

method from a small number of annotated images

by using time series constraints effectively. When

time-series information such as video is available, it

is expected that images which are close in time-series

are similar to each other, and pseudo-labels on those

images are expected to be highly accurate. In other

words, if pseudo-labels are assigned to the images in

the order of time-series, it is possible to efﬁciently

collect pseudo-labels with high accuracy. As a re-

sult, we can improve the segmentation accuracy from

a small number of training images.

We conducted experiments on the Camvid dataset.

We evaluated the accuracy when we train our method

with approximately 1/91, 1/18, and 1/9 of all anno-

tated images. Our method can use the images with

pseudo-labels in the order of time-series for training.

We conﬁrmed the signiﬁcant performance improve-

ment by the proposed method. Speciﬁcally, the pro-

708

Mano, T., Kato, S. and Hotta, K.

Semantic Segmentation by Semi-Supervised Learning Using Time Series Constraint.

DOI: 10.5220/0011721800003417

In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP, pages

708-714

ISBN: 978-989-758-634-7; ISSN: 2184-4321

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

posed method improved the mIoU by 9.33%, 5.7%,

and 3.86% respectively when we use approximately

1/91, 1/18, and 1/9 of all annotated images.

This paper is organized as follows. Section 2

presents related works. Section 3 explains the pro-

posed method. Section 4 provides an experimental

overview, and Section 5 shows the experimental re-

sults. Finally, Section 6 describes conclusions and fu-

ture works.

2 RELATED WORKS

2.1 Semi-Supervised Semantic

Segmentation

Semi-supervised learning is an intermediate method

between supervised and unsupervised learning. Semi-

supervised learning does not require annotation of all

images, so we can reduce the annotation cost. Semi-

supervised learning makes good use of unlabeled im-

ages to improve the accuracy. Speciﬁcally, it has been

attracting attention because pseudo-labels can be as-

signed to unlabeled images.

Pseudo-labeling methods (Lee et al., 2013) cre-

ated pseudo labels by assigning the most probable

class to the pixel in an unlabeled image. In semi-

supervised learning, pseudo-labeling methods (Chen

et al., 2020; Zou et al., 2020) can be used to extend

the training data using unlabeled images.

Semi-supervised semantic segmentation meth-

ods utilize Generative Adversarial Networks (GANs)

(Goodfellow et al., 2020) such as AdvSemiSeg (Hung

et al., 2018) and S4GAN (Mittal et al., 2019). They

use discriminators to distinguish the conﬁdence maps

from predictions for labeled and unlabeled data.

Consistency-based methods include perturbations by

CutMix (French et al., 2019) and ClassMix (Olsson

et al., 2021) to the input image in order to enforce the

consistency of predictions and intermediate features.

However, these methods have the problem that

they are also not designed for video data and do not

use the good property of time-series. Our proposed

method uses time-series constraints that images at

time t and t +1 is similar. By using this constraint ef-

fectively, we can predict more accurate pseudo-labels

easily for unlabeled images in a video.

2.2 Contrastive Learning

Contrastive Learning (Hadsell et al., 2006) has suc-

cessfully improved the accuracy for semi-supervised

learning. Pixel-by-pixel learning was used in the

method (Wang et al., 2021). The pixelwise con-

trastive loss is deﬁned as

NCE

∑

∈P

−log

exp(i·i

/τ)

exp(i·i

/τ)+

∑

−

∈N

exp(i·i

−

/τ)

(1)

where P

and N

denote pixel embedding collections

of the positive samples and negative samples. i is

a pixel, i

represents positive pixel and i

−

repre-

sents negative pixel. Positive and negative pixels are

learned contrastively not only in the same image but

also in two images. The · means the dot product. We

set the temperature τ to 0.1.

It is reported that contrastive learning improves

the accuracy by learning rich semantic relationships

between pixels across different images. In this pa-

per, we also use contrastive learning between pix-

els to predict pseudo-labels with high accuracy be-

cause the model can learn semantic relationships be-

tween pixels across different images. In this study, af-

ter shufﬂing the annotated images and unlabeled im-

ages with pseudo-labels, pixel-wise contrast learning

is performed between images in a mini-batch. In ex-

periments, our method is trained by using both cross

entropy loss and pixel-wise contrastive loss.

3 PROPOSED METHOD

In this section, We explain the details of the pro-

posed method. section 3.1 describes how to create

and learn pseudo-labels using time-series constraints

in only one scene. Section 3.2 describes how to learn

and create pseudo-labels in each scene. Section 3.3

explains the threshold when we predict pseudo-labels

of unlabeled images in a video. Section 3.4 describes

the loss function used in our method.

3.1 Semi-Supervised Learning with

Time-Series Constraints

In the proposed method, the model is trained on some

annotated images in the middle of a scene as shown

in Figure 1. This is because we would like to use the

time-series constraints effectively. In top row of Fig-

ure 1, ﬁve annotated images are used for training the

model. The trained model is applied to two unlabeled

images that are the closest in time-series to the anno-

tated images because images that are chronologically

adjacent to the annotated images are expected to be

similar to the annotated images. Thus, we can assign

highly accurate pseudo-labels to two images.

As shown in the bottom part of Figure 1, train-

Semantic Segmentation by Semi-Supervised Learning Using Time Series Constraint

709

Figure 1: Creating pseudo labels using time-series constraints.

Figure 2: Scene-wise learning and prediction of pseudo labels.

ing is performed again by using the original annotated

images and two pseudo-labeled images. The model

is also applied to two unlabeled images that are the

chronologically closest among the images that have

not yet been assigned pseudo-labels. Then we assign

pseudo-labels to two images. By repeating this pro-

cess in the order of time-series, the accuracy of the

proposed method is gradually improved.

3.2 Scene-Wise Learning and Pseudo

Label Prediction

In general, in-vehicle video dataset such as CamVid

dataset consists of some scenes. If we train a model

using annotated images and pseudo-labeled images in

all scenes simultaneously, we may not use time-series

constraint effectively because the model is not spe-

cialized to one scene. Therefore, in this paper, we

propose to learn and predict pseudo-labels for each

scene as shown in Figure 2.

At ﬁrst, the proposed method is trained using

some annotated images in all scenes. In the Fig-

ure 2, there are four scenes.

⃝

Next, we select one

scene and the model is ﬁne-tuning to the scene.

⃝

Then, we predict the pseudo-labels of two images

that are chronologically adjacent to the annotated im-

ages in the selected scene.

⃝

We pick up the differ-

ent one scene from the ﬁrst scene, and ﬁne-tuning is

performed.

⃝

We also perform the pseudo-label as-

signment. By ﬁne-tuning the model for each scene,

we can use time-series constraint effectively and the

model can predict highly accurate pseudo-labels.

This process is continued for all scenes in the

numbered order shown in Figure 2, and while increas-

ing the number of pseudo-labeled images. Finally,

the model is trained on the annotated images and all

pseudo-labeled images obtained at the upper process.

3.3 Threshold

As described in previous section, pseudo-labels are

assigned to unlabeled images using the time-series

constraint. But, of course, all predictions are not cor-

rect. Thus, a threshold is needed to determine whether

we should assign pseudo-label to each pixel based on

the conﬁdence through softmax function.

If the threshold is high, only the pixels that the

model classiﬁes with high conﬁdence are assigned to

pseudo-labels. However, if the threshold is too high,

only a smaller number of pixels in an image can be

pseudo-labeled.

On the other hand, if the threshold is low, we

must assign pseudo-labels to the pixels with low con-

ﬁdence. But we can increase the number of pixels

for training. Therefore, the setting of threshold is im-

portant for both accurate pseudo-labels and the num-

VISAPP 2023 - 18th International Conference on Computer Vision Theory and Applications

710

ber of pixels for training. In this paper, we tried two

threshold values; a ﬁxed value and an Average Pre-

dicted Value(APV) for each class.

Fixed Value

In general, a ﬁxed value is used as threshold. This

means that only pixels whose conﬁdence exceeds the

ﬁxed threshold are assigned pseudo-labels. In this pa-

per, the threshold for each class was empirically set.

Average Predicted Value

We propose a threshold value called Average Pre-

dicted Value (APV). When ﬁxed values are used, it

is difﬁcult to assign pseudo-labels to pixels with low

conﬁdence, such as the rare class. To compensate for

this, we use the average conﬁdence of outputs of each

class in an image as the threshold value. This allows

approximately half number of pixels classiﬁed in each

class to be assigned pseudo-labels. Since the distribu-

tion of the conﬁdence levels varies for each image,

we can change the threshold automatically for each

image.

3.4 Loss

In this study, the model is trained with the follow-

ing losses by using both annotated images and images

with pseudo-labels. The loss for the proposed method

can be formulated as follows.

L = L

+ λL

NCE

(2)

where L

is cross entropy loss. L

NCE

is pixel wise

contrastive loss showin in equation (1). λ is a hyper-

parameter and is empirically set to 0.1.

4 EXPERIMENTAL SETTINGS

In this section, we explain the experimental settings.

The dataset and training method are described in Sec-

tion 4.1. Experimental conditions are described in

Section 4.2. We explain the baseline method in Sec-

tion 4.3.

4.1 Data Sets and Training Methods

The CamVid dataset (Brostow et al., 2009) used in the

following experiment includes the images of 360 x

480 pixels and 11 classes. CamVid dataset consists of

367 training images, 101 validation images, and 233

test images. The original 367 training images con-

sisted of 5 scenes, but the ﬁfth scene was not used

because the number of images in the scene was too

small to increase the number of pseudo-labeled im-

ages sufﬁciently. Therefore, experiments were con-

ducted using the remaining four scenes. In this paper,

we used 1, 5, and 10 images from each of the four

scenes for training as annotated images. Remaining

images were used as the unlabeled images for pseudo

labels. The 1, 5, and 10 annotated images were the

center images in the order of each time-series scene.

We evaluate whether the proposed method im-

proves the test mIoU. The training images, including

the pseudo-labeled images, were randomly ﬂipped

left and right as data augmentation.

4.2 Network and Parameter

We used Deeplabv3+ with Resnet101 pre-trained on

Imagenet dataset. The batchsize was set to 4, and

SGD (Momentum=0.9, weight

= 1×10

−4

) was used

as the optimization method.

At ﬁrst, the model is trained on the annotated

images in four scenes for 200 iterations. Next, the

trained model is ﬁne-tuned to the speciﬁc scene for

200 iterations. The ﬁne-tuned model is applied to

two unlabeled images in the scene that are chrono-

logically adjacent to the annotated images, and the

pseudo-labels are assigned to the unlabeled images by

using threshold. After we assign pseudo-labels to two

unlabeled images in all scenes, the model is trained

again using the images in all scenes for 200 iterations.

The images with pseudo-labels are used for training

in the next epoch. The process up to this point is de-

ﬁned as 1 epoch. In the second epoch, the model is

trained for 200 iterations using the annotated images

and pseudo-labeled images. This is repeated until all

unlabeled images in scenes are used.

The initial learning rate was set to 0.001 for the

backbone network parameters and 0.01 for the classi-

ﬁer parameters. Then, the learning rate was changed

using the following equation. The learning rate was

attenuated from the initial value at each iteration us-

ing Equation (3).

lr = lr

base

× (1 − iteration/200)

0.9

(3)

We used both cross entropy loss and pixelwise con-

trastive loss as the loss function. Mean IoU (mIoU) is

used as evaluation measure.

4.3 Baseline Method

The baseline method is to train the same model as

the proposed method with the same annotated images

obtained from four scenes. The best model with the

highest mIoU on validation set in 200 iterations is se-

lected as the baseline model. In this experiment, the

Semantic Segmentation by Semi-Supervised Learning Using Time Series Constraint

711

Table 1: Comparison of the proposed method and baseline on Camvid test dataset.

4/367 20/367 40/367

class baseline ours baseline ours baseline ours

sky 85.73 89.75 (+4.02) 89.57 89.76 (+0.19) 90.18 90.62 (+0.44)

building 67.91 73.10 (+5.19) 72.42 73.84 (+1.42) 74.33 75.15 (+0.82)

pole 4.08 12.67 (+8.59) 13.37 14.45 (+1.08) 14.23 16.65 (+2.42)

road 71.91 78.88 (+6.97) 78.53 87.67 (+9.14) 84.27 91.49 (+7.22)

sidewalk 16.15 39.60 (+23.45) 35.52 61.77 (+26.25) 53.67 73.71 (+20.04)

tree 55.17 67.38 (+12.21) 63.44 65.71 (+2.27) 65.55 67.67 (+2.12)

signal 1.11 14.35 (+13.24) 13.57 13.00 (-0.57) 31.35 30.56 (-0.79)

fence 0.00 0.16 (+0.16) 1.69 5.59 (+3.9) 8.95 13.66 (+4.71)

car 48.30 60.22 (+11.92) 62.31 73.17 (+10.86) 70.58 75.90 (+5.32)

pedestrian 3.10 19.94 (+16.84) 24.15 32.78 (+8.63) 36.56 42.14 (+5.58)

bicyclist 0.00 0.00 (+0.00) 0.01 0.00 (-0.01) 7.74 2.29 (-5.45)

mIoU 32.13 41.46 (+9.33) 41.33 47.07 (+5.74) 48.86 52.71 (+3.85)

effectiveness of the proposed method is demonstrated

by comparing the test mIoU of the baseline method

with the test mIoU of the proposed method when the

number of pseudo-labeled images is increased.

5 EXPERIMENTAL RESULTS

We conducted an experiment to evaluate semi-

supervised semantic segmentation on the CamVid

dataset. Section 5.1 shows the accuracy and quali-

tative comparison of the baseline and the proposed

method. Section 5.2 shows the results of ablation

studies.

5.1 Comparison of the Proposed

Method with Baseline

Table 1 shows the IoU of each class and mIoU by

baseline and the proposed method. The column of

baseline shows the IoU of each class by baseline. The

column of ours show the IoU and improvement com-

pared with baseline. 4/367, 20/367, and 40/367 indi-

cate the number of annotated images actually used for

training. For example, 4/367 means that we use only

one annotated image in one of four scenes. 20/367

means that we use 5 annotated images in each scene.

By using the proposed method, we were able to

signiﬁcantly improve the mIoU compared to the base-

line. When we used only one annotated image in each

scene, the mIoU was improved 9.33%. The mIoU of

our method at this setting was better than the mIoU

of baseline at the setting that 5 annotated images per

scene are used for training. The IoUs of sidewalk,

tree, signal, and car are particularly improved. The

classiﬁcation of Pedestrian and Bicyclist is difﬁcult

because both Pedestrian and Bicyclist are very small

and similar. In experiments, many Bicyclist was mis-

classiﬁed as Pedestrian. Thus, Bicyclist class had low

accuracy and Pedestrian class was much improved.

Although our method improved the accuracy in

all settings, the accuracy was signiﬁcantly improved

with fewer annotated images. Since the goal of semi-

supervised learning is to learn with as fewer super-

vision as possible, the effectiveness of our proposed

method is demonstrated.

Figure 3 shows the qualitative segmentation re-

sults of the baseline and the proposed method when

we used 40 annotated images. Our proposed method

generates more accurate segmentation results than the

baseline. We see that sidewalks are predicted more

accurately using the proposed method than baselines.

In addition, there are many parts of the baseline

where the pedestrian is not predicted, but the pro-

posed method is able to predict the pedestrian more

accurately. These results demonstrated the effective-

ness of the proposed method.

5.2 Ablation Studies

Table 2: Ablation study on the loss function. We show the

improvement from baseline in test mIoU by the proposed

method when we used 20 annotated images.

NCE

mIoU

✓ +2.27

✓ ✓ +5.74

Table 3: Comparison of threshold when we used 20 anno-

tated images.

threshold 0.2 0.4 0.6 0.8 APV

mIoU 43.25 43.33 43.99 44.77 47.07

Table 2 shows ablation study about the loss functions.

Table shows the improvement from baseline in test

VISAPP 2023 - 18th International Conference on Computer Vision Theory and Applications

712

Figure 3: Comparison of qualitative results by the baseline and the proposed method when we used 40 labeled images for

training.

mIoU by the proposed method when we used 20 an-

notated images in four scenes. We see that contra-

sitive loss improved the accuracy signiﬁcantly.

Table 3 shows a comparison of the threshold when

we use 20 annotated images. We see that the mIoU

increases signiﬁcantly when the Average Predicted

Value(APV) of each class is used as the threshold

value. If a ﬁxed value is used for the threshold value,

the accuracy is biased toward the classes with large

training samples, because the classes with small train-

ing samples have a smaller probability than classes

with large training samples. However, by using aver-

age predict value as the threshold value, we can use

different threshold value for each class, and this im-

proved the accuracy.

6 CONCLUSIONS

We proposed a semi-supervised semantic segmenta-

tion method that assigns pseudo-labels in chronolog-

ical order and trains the model using those images

step by step. We conﬁrmed that our proposed method

much improved test mIoU on the Camvid dataset in

comparison with the baseline model.

Our proposed method used only the network’s

output to assign pseudo-labels. However, prior proba-

bility, which is calculated from past images in a time-

series manner, may improve the accuracy of pseudo-

labels. The usage of prior probability is one of the

subject for future works.

ACKNOWLEDGEMENTS

This work is supported by JSPS KAKENHI Grant

Number 21K11971.

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