SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road
Segmentation in Hazardous Environments
Divya Kothandaraman, Rohan Chandra, Dinesh Manocha
University of Maryland, College Park
Tech Report, Code, and Video at https://gamma.umd.edu/weatherSAfE/

Abstract
We present a novel approach for unsupervised road segmentation in adverse weather conditions such as rain or
fog. This includes a new algorithm for source-free domain
adaptation (SFDA) using self-supervised learning. Moreover, our approach uses several techniques to address various challenges in SFDA and improve performance, including online generation of pseudo-labels and self-attention as
well as use of curriculum learning, entropy minimizationand model distillation. We have evaluated the performanceon 6 datasets corresponding to real and synthetic adverse
weather conditions. Our method outperforms all prior
works on unsupervised road segmentation and SFDA by atleast 10.26%, and improves the training time by 18 − 180×.
Moreover, our self-supervised algorithm exhibits similar
accuracy performance in terms of mIOU score as compared
to prior supervised methods.

1. Introduction
Research in autonomous driving continues to advancein terms of improving the perception capabilities of selfdriving cars for greater safety. This includes detecting both
static and dynamic obstacles such as pedestrians [16], tracking and predicting the trajectories of other vehicles [4, 7,
6, 5], and scene segmentation [18, 62]. Immense progressalong these lines has led to the deployment of level 2 andalmost level 3 autonomous vehicles (AVs) in urban traffic environments [13]. However, these advances in perception technology have been primarily designed to work wellin safe and clear weather conditions. Driving in adverse
weather and lighting conditions such as snow, rain, or fog ischallenging not just for autonomous vehicles but even forhumans. These conditions result in a degradation in accuracy of perception techniques including road segmentation [43, 42]. Consequently, AVs are unable to distinguishdrivable regions of the road from the non-driveable region(which may be affected by snow, rain, or fog), thereby increasing the likelihood of road accidents [36]. In this paper, we address the problem of road segmentation in adverse
weather conditions.
The road segmentation [17, 48] problem corresponds toidentifying the pixels in an RGB image or video that be-

Figure 1: We highlight the results generated by SS-SFDA onnight [42] and fog benchmarks [43], compared to the baseline
source model pre-trained on clear weather CityScapes. The purple regions (right) denote the segmented road pixels. The overall accuracy of our self-supervised algorithm in terms of mIoU is(88 − 96%) of supervised methods.

long to the ‘road’ class. While general models designed forsemantic segmentation in computer vision can be directly
used for road segmentation, they suffer from the inability
to capture semantic relationships between different objects
due to the lack of unique labels for each class. The use of
self-attention techniques [18, 60] can mitigate this issue by
capturing long-range dependencies.
However, one major challenge in road segmentation inadverse weather is the lack of ground-truth annotations
for road pixels. A common approach in deep learningfor handling lack of training data is domain adaptation(DA) [22, 52]. However, DA-based methods assume access to source datasets (clear weather dataset in our context) at all times which can be prohibitive in terms of storage, memory, data corruption and privacy concerns. Recently, [26, 37] have proposed source-free domain adaptation (SFDA) in which deep neural networks (DNNs) do
not require access to the source dataset during the adaptation stage; instead, DNNs are pre-trained on a source
dataset (clear weather dataset) and the pre-trained model isdirectly used to adapt to the unlabeled target domain (adverse weather dataset).
Current methods for SFDA are used for image classification [28, 24, 29, 56] and may not work well for semantic
segmentation due to the inherent differences between theclassification and segmentation tasks. Moreover, many current SFDA methods use GANs to produce a “copy” of theoriginal source domain distribution. In addition to being

computationally intensive due to the difficulty in training
GANs, image generation for segmentation requires GANs
to capture contextual information and semantic relationships between multiple objects and the background, which
can be complicated in road scenes. As a result, prior SFDA
techniques have not been used for road segmentation.

1.1. Main Contributions
We present a new approach for road segmentation inadverse weather conditions. Our approach is based on anovel algorithm for SFDA using self-supervised learning.
We initialize our model with an auto-encoder baseline network using self-attention to generate a pre-trained modelon the clear weather source dataset. Using self-attention
improves the overall model by capturing long-range dependencies within the image (Section 3.1). Our novel contributions include:
1. We present a novel two-step self-supervised SFDA approach called SS-SFDA. In the first step, our method
uses entropy minimization to enrich the noisy pseudolabels generated by the pre-trained auto-encoder. In
the second step, we use a novel self-training methodthat generates pseudo labels in an online manner, as
opposed to iterative self-training use by prior methods (Section 3.2). We use curriculum learning to implement these two steps. This results in the following
benefits compared to prior GAN-based approaches:• SS-SFDA directly exploits the pre-trained modeland trains via curriculum learning to progressively bridge the domain gap between the pretrained source domain and target domain andachieve faster training times.
• Our online self-training scheme overcomes thesaturation issues.
2. For heterogeneous adverse weather datasets, we propose a method that extends SS-SFDA by leveraging a
few labeled images from the target domain to improve
the accuracy using model distillation (Section 3.3).
We have evaluated our approach on 6 datasets corresponding to real and synthetic adverse weather conditions.
Overall, our mIoU score is 88 − 96% of prior supervised
methods. We also improve the training time over prior
SFDA approaches by 18 − 180×. Finally, our improvementin terms of mIoU over the best SFDA approach is 10.26%
on real adverse weather data.

2.2. Domain Adaptation and Source Free Domain
Adaptation
Traditional domain adaptation [23, 22, 45, 53, 50, 10]
methods have achieved remarkable success in adapting
models from one domain to another for clear weather conditions. However, these methods need access to the source
data. Many domain specific solutions have been proposed
for adverse weather conditions, including specific solutions
for driving in rain, fog, etc. [40, 36, 43, 14, 44, 39] In contrast, we propose a generic method that neither relies on
specific details from each domain, nor requires access tosource data during the adaptation stage.
In source-image free domain adaptation (SFDA), a deepneural network (DNN) pre-trained on a source dataset isrequired to directly make predictions on the target domain
dataset in an unsupervised manner. This approach has been
primarily used for image classification tasks. In SFDA, generative approaches [28, 24, 29, 56, 31] are used to either
emulate the source data by using the feature representations of the pre-trained model or create a negative source
dataset during the pre-training stage. Non-generative approaches [26, 55, 33, 25] rely on computing adaptive classspecific prototypes, and progressively learn on the target
images. While generative approaches work well for classification tasks, they have not been effective for urban scene
segmentation. This is because segmentation, being a pixel
level task, requires the network to encode context, interclass semantic relations, structure, and intricate boundaries [15, 59, 63]. In addition, training a generator for acomplex segmentation task can lead to memory and computational overheads, and thereby adds to the difficulty of
training GANs. While non-generative classification methods are easier to adapt for segmentation, the computationof prototypes results in similar issues due to the inherent
differences between classification and segmentation tasks.
Bateson et al. [1] explored SFDA in the context of medical segmentation and minimized entropy by the incorporation of class priors [1]. However, preservation of class priors does not extend well to urban road scenes because thenumber of road pixels in each image in the dataset can varyquite drastically. In contrast, we present a new method for
SFDA using self-supervised learning that overcomes issues
related to training and convergence that are freq generative
processes. Our method is designed to be complimentary to
GAN-based SFDA.

2. Related WorkWe discuss recent work related to road segmentation,
domain adaptation and source-free domain adaptation, andself-supervised learning.

2.1. Road Segmentation
Research in deep learning for semantic segmentation [34, 57, 8, 9, 62, 18, 49] has paved the way for segmentation in urban traffic scenes like CityScapes [12]. Thesemethods have been extended for supervised road segmentation [54, 64, 17, 48]. Our approach based on self-supervised
learning is complimentary to these methods.

2.2.1

Self-Supervised Learning

Self-supervised learning has been used in semi-supervised
learning [32, 3] and domain adaptation [46]. Most selfsupervised learning methods are centered around the ideasof pseudo labeling [30, 11, 35, 65, 27], entropy minimization [52, 19], and curriculum learning [61]. Domain adaptation methods have access to source domain images. In
contrast, we propose a novel self-training routine for SFDA
and a completely unsupervised problem setting, where wehave access to only a pre-trained model, and target domain
images.

Figure 2: Our Approach: In stage 1, our model is pre-trained on a clear weather source dataset. In stage 2, our model is initialized withthe pre-trained model from stage 1 and trained using our self-supervised algorithm, SS-SFDA, on the unlabeled adverse weather dataset.
For heterogeneous weather datasets, we perform additional refinement steps based on model distillation (stage 3).

3. Our Approach
In this section, we present our approach for source-image
free domain adaptive (SFDA) road segmentation based on
self-supervised learning. Our approach consists of threemain components:
1. Pre-training using the self-attention auto-encoder:
During this stage, we train the self-attention autoencoder architecture on a clear weather dataset. This
generates a model that encapsulates knowledge aboutroad pixels.
2. SS-SFDA : A Self-Supervised learning algorithm
for SFDA: During this stage, we initialize the modelusing the pre-trained model from the previous stepand the target domain images. We use a combination of curriculum learning and entropy minimization
to bridge the domain gap between the pseudo-labels
and the target domain images. We first sort the target
domain images in the increasing order of entropy, andcreate mini-batches of the dataset. The next task is to
execute the following steps on each mini-batch to progressively self-train the model:• Optimize the model with an entropy minimization constraint to bridge the domain gap.
• Self-train the model by generating enrichedpseudo-labels in an online manner.
3. Few-Image Regularization: For heterogeneous
weather datasets, we use a very small number of labeled images (5 − 10) from the target domain to boost
the performance of SS-SFDA via model distillation.
(Section 3.3)

3.1. Pre-Training Baselines Using Self-Attention
The first step in SFDA is to pre-train a DNN on thesource dataset for the task of road segmentation. In ourcase, the source dataset corresponds to traffic videos withclear weather conditions. While networks developed for semantic segmentation [49, 62, 8, 9] can be directly used forroad segmentation, there is loss of context i.e. the modelis unable to capture relationships between various semantic
classes in the images like cars and roads, pedestrians androads, sky and roads, etc. The loss of such context can leadto local ambiguities in classifying pixels [15, 59, 63].
Self-attention benefits from its capability to capture
long-range dependencies between various regions of the image. Thus, using self-attention in road segmentation can allow neural networks to alleviate the degradation in performance due to loss of context. We use a simple autoencoder
self-attention architecture that can be combined with any“off-the-shelf” segmentation network. We begin by taking
an input RGB image I ∈ Rw×h×3 , which is passed throughan encoder E to generate feature maps Fen (Fen = E(I)).
Next, we apply self-attention SA [60] on these features
maps to obtain attention maps Fsa (same dimensions as
Fen ). These feature maps encapsulate the semantic relationships between various parts of the image. These feature maps Fsa are used to learn the final predictions Pout′′
∈ Rw ×h ×1 , which corresponds to the probability that eachpixel is classified as ‘road’. In the supervised setting where′′
ground-truth labels Y ∈ Zw ×h ×1 are available, the network is optimized with a binary cross-entropy loss function,
  \mc {L}_{\textrm {CE}} = - \sum _{h,w} \mc {Y}\log (P_{out}) + (1-\mc {Y})\log (1-P_{out}). \label {eq: cross_entropy}  (1)We use this pre-trained model for SFDA on the adverse
weather datasets [43, 42, 58, 21, 51].

3.2. SS-SFDA
In this section, we describe our two-step self-supervised
learning algorithm for unsupervised source-image free domain adaptive road segmentation in adverse weather conditions. We use the pre-trained model from the previous step.
The pseudo-labels generated by the model are noisy leading to a domain gap between the pseudo-labels generated
by the pre-trained model (on the source dataset) and the target domain images. Thus, directly self-training using thepseudo labels can hamper the performance of the model.
To counter this, we propose an entropy minimization step
(Section 3.2.1) which encourages the network to generate
more accurate pseudo labels.
In addition, to bridge the domain gap between the pre-trained model and the target domain, we use curriculum
learning [2, 20] in which the DNN is allowed to train on
samples progressively in their increasing order of entropy of
predictions. Given a probability map P denoting the probability that pixels are classified as road pixels, the entropy iscomputed as −ΣP × log(P ) This is because learning fromsamples with low entropy (low rain, for example) yields
better pseudo labels on samples with higher entropy (highrain, for example) [14, 61, 61]. We create mini-batches ofthe dataset characterizing the difficulty of the images. For
datasets which provide labels on the intensity (light rain vsheavy rain) of the weather condition, mini-batches can becreated directly. For other datasets, we sort the images inincreasing order of the entropy and then split them into m(m ∼ 4 − 5, determined by hyperparameter tuning) equalmini-batches. The model is self-trained on the mini-batches
in a sequential manner. For the first mini-batch, the modelis initialized with the pre-trained model from Section 3.1.
For subsequent mini-batches, our model is initialized withweights obtained by training the network on the previous
mini-batch. For each mini-batch, the network is trained in
two stages, as described below:
3.2.1

Step 1: Bridging the Domain Gap via Entropy
Minimization

The pre-trained model from Section 3.1 has a low entropy(i.e. high prediction probability or better generalization)
on images that are similar (for example, similar geography, light rain, light fog) to source domain images [42]and vice versa. Thus, initializing the network with thesepre-trained weights, followed by training by entropy minimization [19, 41] allows the network to generate enrichedpseudo-labels. The inputs to the network are images fromthe target domain. Let the predictions of the network be de′′noted by Pout ∈ Rw ×h ×1 , corresponding to the probability that each pixel is classified as ‘road’. The cost function
for entropy minimization is given by,  L_{\textrm {EM}} = - \sum _{\forall \textrm {pixels}}P^{h,w} \log P^{h,w}, (2)where P h,w is the probability that a pixel belongs to a class‘road’ at a given location, and −P h,w log, P h,w is the entropy.
3.2.2

Step 2: Online Self-Training Using Enriched
Pseudo-Labels

The network trained in Step 1 generates enhanced pseudolabels with high probability, and is thus a better representative of the target domain than the pre-trained source modelfrom Section 3.1. These enriched pseudo labels which canbe used to self-train the model further to improve performance. A traditional method of self-training using pseudolabels is iterative [38] in which the network is trained to convergence (validation loss less than a given threshold) overmultiple iterations. In each iteration, pseudo labels from thetrained model in the previous iteration are used to set up theDatasetRainy CityScapes [21]Foggy CityScapes [21]Foggy Zurich [43]Dark Zurich [42]Raincouver [51]
BDD [58]

Syn./Real

Weather

Syn
Syn
Real
Real
Real
Real

Varying intensities (1mm - 200mm) of rain
Varying intensities (750m - 30m) of fog
Light and medium Fog
Twilight, Night
Rain, nightSnow, Fog, Rain, Night

Table 1: List of datasets: The second column categorizes thedatasets as synthetic or real and the third column describes theimages contained in the dataset.

binary segmentation cost function. We show (Table 10) that
iterative self training does not lead to any improvement in
performance. This is because, the pre-training step, which
is imperative for acquiring initial knowledge about road pixels since the problem is unsupervised in the target domain,causes the network to saturate quickly. Hence, we generate
the pseudo labels in an online manner, i.e. the pseudo labels
are generated from the network that is being trained. This
allows the network to self-train from the improved pseudolabels as they are learnt.
The network in this stage is initialized with the weights
obtained in Step 1. The inputs to the network are images
from the target domain. Pseudo labels are generated in an
online fashion from the network being trained as follows,
  Y_{\textrm {pseudo}} = \begin {cases} 1 \ \textrm {if} \ P^{h,w} \geq \tau , \\ 0 \ \textrm {otherwise}, \end {cases} where P h,w is the probability that a pixel belongs to theclass ‘road’ at a given location and τ is a threshold. The
network is optimized with these pseudo-labels using a binary cross entropy loss term, (similar to Equation 1).

3.3. Few-Image Fine-Tuning via Model Distillation
Some heterogeneous weather datasets like Raincouver[51] and Berkeley Deep Drive (BDD) [58] contain a mixture of adversities within the same image (for instancenight+rain in Raincouver, see Table 1). Furthermore, thesedatasets are captured from different geographic conditions(i.e., source and target datasets may be from different regions). To make our model robust against such factors, we
use ground truth labels for a few images (order of 5 − 10
images) from the target dataset in a final refinement step
described below. In a nutshell, given a model trained on
the unlabeled target dataset using SS-SFDA, and k ≤ 10labeled images from the target domain images, our goal is
to learn enhanced feature maps for the target domain in thepresence of adversarial factors such as mixtures of adversities and different geographical regions.
We empirically observe that directly fine-tuning the SSSFDA model on the k images is sub-optimal due to overfitting. To prevent overfitting, we propose a model distillation [31] regularizer. Let the weights of the SSSFDA model be denoted by ωSS-SFDA , and the weights ofthe model being currently trained be denoted by ωfewIm . The
cost function for model distillation is given by,  L_{\textrm {model-distil}} = C(\omega _{\textrm {\model }},\omega _\textrm {fewIm}), ModelDeepLabv2 [8]DeepLabv2 (E-1) [8]
DeepLabv2 (E-2) [8]DRN-D-105 [57]
DRN-D-38 [57]Acc.(%) (w/o. SA)

Acc.(%) (w. SA)

89.59
87.50
88.13
83.92
90.69

90.54 (+0.95)
90.78 (+3.28)
88.62 (+0.49)
85.32 (+1.40)
91.44 (+0.75)

Intensity

Table 2: Effect of self-attention: We show for various backbone architectures that self-attention improves the accuracy.
DeepLabv2 (E-1) and DeepLabv2 (E-2) denote the removal of 1
and 2 layers from DeepLab respectively. We select DRN-D-38
with self-attention (bolded) as the baseline for all further experiments.

where C represents a distance function such as MSE distance or L1 distance. In our benchmarks, MSE distanceworks best.
The network is first initialized with weights of the SSSFDA model. The model distillation term Lmodel-distil withweight parameter λmodel-distil is applied in conjunction withthe binary cross entropy loss function (Equation 1) to constrain the probability predictions and ground-truth labels fork images. The λmodel-distil term balances between extracting domain specific characteristics from the k images (suchas mix of adverse weather, geographical features etc.) andprevents the weights of the model from diverging from the
SS-SFDA weights (for better generalization). The overall
equation follows as,  \mathcal {L}_{\textrm {overall}} = L_{\textrm {CE}} + \lambda _{\textrm {model-distil}} L_{\textrm {model-distil}} mIoU

Recall

Prec.

F1

96.86
96.92
96.85
96.42
96.43
95.47
94.56
94.42

96.49
96.93
96.86
96.41
96.08
95.86
95.60
95.05

97.97
97.51
96.74
95.15
95.16
94.03
94.02

97.71
97.30
96.22
95.63
95.06
94.85
94.18

87.19
95.76

83.64
94.29

I. Synthetic Rain [21](3)

4. Experiments and Results
We use the CityScapes dataset as the clear weather
source domain. We conduct evaluation experiments on 6
datasets captured in adverse environmental conditions, described in Table 1. We evaluate our model using four metrics: mean Intersection over Union (mIoU), Recall (or accuracy), Precision, and F1 score. All our models are trainedusing one NVIDIA GeForce GPU, and we implement themodel using the PyTorch framework. We will make allcode publicly available. The hyperparameters generalize
across our experiments on all datasets. For the segmenatation model, we use the SGD optimizer with a learning rateof 2.5e − 4, and momentum of 0.9 and weight decay of
0.0005. Dataset specific details are provided in the table below. Images are downsampled (by a factor of 2, where necessary) by bilinear sampling, and the corresponding groundtruth labels are downsampled by nearest neighbour downsampling.

4.1. Analysing Pre-Trained Self-Attention-based
AutoEncoder
In this section, we analyse the performance of our selfattention auto-encoder model (described in Section 3.1 andbenchmark its performance on various datasets in the supervised setting. In Table 2 (I), we show that the usageof self-attention within various conventional semantic segmentation models to encode semantic relationships via capturing long-range dependencies improves performance overthe corresponding baseline. We use the DRN-D-38 model

1mm
5mm
17mm
25mm
50mm
75mm
100mm
200mm

93.23
94.06
93.92
93.07
92.45
92.05
91.57
90.57

96.13
96.95
96.87
96.4
95.72
96.25
96.66
95.69

II. Synthetic Fog [21]
750m
375m
150m
75m
50m
40m
30m

95.53
94.74
92.72
91.64
90.59
90.21
89.00

97.46
97.08
95.71
96.12
94.97
95.68
94.33

III. Real datasets
Raincouver [51]
BDD [58]

71.88
89.19

80.36
92.86

IV. SOTA ComparisonsDatasetMethodmIoU

RecallF1

CityScapes

FCN [34]
CA [18]
BoT [47]
DeepLabv3 [9]s-FCN-loc [54]Zohourian [64]RBA [48]
Ours

89.90
88.93
76.55
90.87
91.04
86.34
96.00
91.44

95.70
94.05
85.82
95.35
96.11
96.76
98.13
96.34

94.68
94.14
86.72
95.21
95.36
92.44
98.00
95.52

100mm Rain

SNE-Seg [17]
Ours

90.80
91.57

96.80
96.66

95.80
95.60

100mm Rain

SNE-Seg [17]
Ours

90.50
91.64

93.00
96.12

94.47
95.63

Table 3: Benchmarking and comparing the self-attention based
pre-trained model: We train our self-attention model from Section 3.1 on various datasets in a supervised manner. These supervised numbers help us conduct a relative study of the performanceof SS-SFDA , which is unsupervised in the target domain. Additionally, we observe that our pre-trained model is comparable tothe state-of-the-art (Experiment IV). Best results are in bold fonts,
second best results are in blue).

[57] with self-attention in all further experiments. In Table 3 (I,II,III), we benchmark the model on various weather
datasets in the supervised setting. We use these supervised
numbers in the following subsections to perform a comparative study of our self-supervised model (See Figure 1 for asummary). In Table 3 (IV), we show that the self-attention
autoencoder is comparable to the state-of-the-art on various
datasets.

4.2. Results on Synthetic Datasets: Rain and Fog
We perform three evaluation experiments. Experiment
A corresponds to testing the pre-trained CityScapes model
on varying intensities of rain and fog. Experiment B corresponds to results obtained by training SS-SFDA without
curriculum learning (i.e. by initializing with the CityScapes
pre-trained model and training on higher intensities of rain
and fog directly), and experiment C corresponds to results

Intensity

ExperimentmIoU

RecallPrec.

F1

I. Synthetic Rain
1 mm
5 mm
17 mm
25 mm
50mm

A
A
A
A
A

94.73
94.09
93.95
93.01
89.82

97.16
97.01
96.83
96.30
93.71

97.42
96.90
96.93
96.46
95.58

97.29
96.95
96.88
96.38
94.64

75mm
75mm

A
B

87.15
88.08

90.92
93.94

95.45
93.38

93.13
93.66

100mm
100mm

A
B

82.97
86.67

86.88
92.88

94.84
92.84

90.69
92.86

200mm
200mm
200mm

A
B
C

65.98
80.25
81.58

68.94
86.96
88.55

93.90
91.22
91.20

79.50
89.04
89.85

750m
375m

A
A

94.90
92.71

96.68
94.60

98.09
97.88

97.38
96.21

150m
150m

A
B

85.55
89.74

87.57
94.59

97.38
94.59

92.21
94.59

75m
75m
75m

A
B
C

70.81
80.67
87.48

72.51
83.01
92.99

96.79
96.62
93.65

82.9
89.30
93.32

50m
50m
50m

A
B
C

57.29
71.64
85.47

58.92
73.69
90.75

95.39
96.24
93.63

72.85
83.47
92.16

40m
40m
40m

A
B
C

46.27
52.62
83.65

48.45
53.94
89.19

91.12
95.57
93.09

63.27
68.96
91.10

30m
30m
30m

A
B
C

35.04
22.24
80.82

38.05
22.92
85.96

81.59
88.29
93.11

51.90
36.39
89.39

II. Synthetic Fog

Table 4: Results on synthetic rain and synthetic fog [21]: We
evaluate our model under three settings - A: (Baseline) Testing theclear weather CityScapes model, B: SS-SFDA without curriculum
learning, C: SS-SFDA. We show that the CityScapes model generalizes well to light synthetic rain and fog, and its performancedegrades as the intensity of rain and fog increases, which is restored by SS-SFDA. Comparison to supervised results: On synthetic rain, SS-SFDA achieves 90.07%-90.8% and 92.53%-97.6%
of supervised mIoU and recall, respectively. On synthetic fog, SSSFDA achieves 90.8%-96.78% and 91.12%-98.82% of supervised
mIoU and recall respectively.

obtained by training SS-SFDA as proposed in Section 3.2(i.e. with curriculum learning).
Results on Synthetic Rain: The results of SS-SFDA on
Synthetic Rainy CityScapes [21] are shown in Table 4(I). For low intensities of rain, the performance of the
CityScapes model is more or less preserved (Table 3 (I)).
For higher intensities of rain, there is a degradation in
performance. We observe that the decrease in performance is highest for 200mm rain, at 27.15%. For 75mm,
100mm, and 200mm, Experiment B imparts an improvement of 1.03%, 4.45%, and 21.63% respectively over thecorresponding baselines. Experiment C leads to a cumulative improvement of 23.64% over the corresponding baseline. Furthermore, we demonstrate that the mIoU and recall of our SS-SFDA, which is completely unsupervised, is90.07% − 90.80% and 92.53% − 97.60% of the counterpart

supervised mIoU.
Results on Synthetic Fog: The results of our SS-SFDA on
Synthetic Foggy CityScapes [21] are shown in Table 4II.
Similar to synthetic rain, the performance of the CityScapesmodel on light fog is more or less preserved (Table 3 (II)).
For higher intensities of fog, there is a degradation in performance. We notice that the degradation is very high for visibility distances less than 150m, and is the highest at 60.6%
for 30m fog. Experiment B leads to an improvement of
4.89%, 13.9%, 25.04%, 13.7% on 150m, 75m, 50m, and
40m fog respectively. Direct application of the self-training
algorithm on 30m fog (without curriculum learning) degrades performance since the generalization of CityScapeson 30m is very poor. Experiment C (Training SelfTr-Road
Seg by curriculum learning i.e. progressively from 150m
fog to 30m fog) cumulatively improves performance overthe baselines (Experiment A) by 23.54%, 28.18%, 80.78%
and 130.65% on 75m, 50m 40m, and 30m fog respectively.
Furthermore, we demonstrate that the mIoU and recall of
our SS-SFDA, which is completely unsupervised, is 90.8%96.78% and 91.12%-98.82% of the supervised mIoU.
Benefits of Curriculum Learning: On a given dataset, theperformance of our self-training algorithm depends heavily
on the generalization capabilities of the pre-trained model
used for initialization. Therefore, progressively initializing and training the model on increasing intensities of rain
and fog will lead to the best accuracies (Table 4, Table 4).
This implies that progressively training from low intensities to high intensities of rain and fog improves the quality of pseudo labels and prediction probabilities on high intensities of rain and fog. In simpler terms, for the 100mm
rain dataset, a model trained on 75mm rain will work better than the CityScapes clear weather model. Similarly, forthe 200mm rain dataset, a model trained on 100mm rain
will work better than the 75mm rain model, which in turnwill generalize better the CityScapes clear weather model.
A similar intuition can be drawn for synthetic fog too. We
validate this hypothesis in Table 5. The first and second
columns correspond to the datasets that the model is trained
and tested on respectively. We observe that curriculum
learning progressively improves performance, which resultsin high quality pseudo labels with high confidence, a boon
for self-training.
Generalization trends: We observe that our selfsupervised SS-SFDA preserves the accuracy on the clear
weather source dataset, CityScapes. The mIoU and accuracy of the fog model on clear weather CityScapes are at
96.9% and 99.00% of the supervised CityScapes model, respectively. The corresponding accuracy numbers for synthetic rain are 98.11% and 98.72%, respectively.

4.3. Results on Real datasets: Foggy Zurich andDark Zurich
Analysis: Foggy Zurich The results are presented in Table
6. The pre-trained CityScapes model fails to generalize (Table 6 (I)) to the real fog dataset due to the domain gap. In
accordance with the curriculum learning strategy, the selftraining algorithm is first applied on images with light fog,
and then on images with medium fog. We show results

(a) Fog 50m(b) Baseline(c) Ours(d) GT(e) Dark Zurich(f) Baseline(g) Ours(h) GT(i) Rain 100mm(j) Baseline(k) Ours(l) GT(m) Foggy Zurich(n) Baseline(o) Ours(p) GT

Figure 3: Qualitative results. Our model generates results that closely resemble the ground-truth (GT) compared to the baseline CityScapes
pre-trained model. Purple indicates the segmented road region. More results can be found in the supplementary material.

Training

TestingmIoU

Recall

Prec.

F1

CS
75mm rain

100mm rain
100mm rain

82.97
86.31

86.88
92.63

94.84
92.68

90.69
92.65

I. Baseline: CityScapes pre-trained modelCS
75 mm rain
100 mm200mm rain
200mm rain
200mm rain

65.98
78.97
79.61

68.94
85.86
86.27

93.9
90.77
91.15

79.5
88.25
88.65

II. SS-SFDA on Twilight Zurich; Init: CS modelCS
150m fog

75m fog
75m fog

70.81
83.82

72.51
87.44

96.79
95.28

82.9
91.19

Step 1
Step 2CS150m fog
75m fog

50m fog
50m fog
50m fog

57.29
76.07
84.38

58.92
79.49
89.44

95.39
94.64
93.71

72.85
86.41
91.52

CS
150m fog
75m fog

40m fog
40m fog
40m fog

46.27
65.6
78.17

48.45
69.98
83.51

91.12
91.28
92.43

63.27
79.22
87.74

CS
150m fog
75m fog

30m fog
30m fog
30m fog

35.04
53.61
68.49

38.05
59.62
75.21

81.59
84.17
88.45

51.9
69.8
81.3

Table 5: Curriculum learning improves performance by improving the accuracy of pseudo labels: The first and second
columns correspond to the datasets our model is trained and tested,
respectively. Evaluating on 30m fog reveals that a model finetuned
to 75m fog performs better than the counterpart 150m fog model,
which in turn performs better than that the CityScapes model.
Similar conclusions can be drawn for other intensities of rain/fog.
ExperimentmIoU

RecallPrec.

F1

I. Baseline: CityScapes pre-trained model
-

36.91

57.04

51.13

53.92

II. SS-SFDA on Light Fog; Init: CS model
Step 1
Step 2

59.1
60.9

68.27
69.09

81.48
83.7

74.29
75.7

III. SS-SFDA on Medium Fog; Init: Light fog model;
Step 1
Step 2

73.34
74.5

76.22
76.74

95.1
96.23

84.62
85.39

Table 6: Results on the real fog dataset, Foggy Zurich. We
observe that SS-SFDA improves mIoU by 101.84% over the corresponding baseline. We notice that training the model on light
fog followed by medium fog imparts the model with improvised
self-training abilities, the mIoU improves by 22.3%. Additionally,
the ablations on each minibatch indicate that the two step training
procedure in SS-SFDA is indeed helpful.

for each of the two stages of SS-SFDA to demonstrate how
the two-step training procedure gradually improves perfor-

Experiment

mIoU

-

Recall

54.9

69.11
69.13

63.89

74.75
74.77

Prec.

79.6

90.15
90.16

F1

70.88

81.74
81.74

III. SS-SFDA on Night Zurich; Init: Twilight model
Step 1
Step 2

71.75
72.18

76.22
76.51

92.44
92.72

83.55
83.84

Table 7: Results on the night driving dataset, Dark Zurich. We
observe that SS-SFDA improves mIoU by 31.47. We observethat our two-stage training routine within the curriculum learningalgorithm helps the network perform well.

ExperimentmIoU

RecallPrec.

F1

I. SS-SFDA

52.42

93.96

54.25

68.78

II. Results on FewIm-FT (Section 3.3)
k=1
k=2
k=10

54.71
56.74
63.85

90.7
90.45
79.39

57.96
60.36
76.54

70.74
72.4
77.93

III. Effect of model distillation (MD); k=10
Without MD
With MD

57.08
63.85

70.99
79.39

74.45
76.54

72.67
77.93

Table 8: Results on Raincouver: SS-SFDA achieves 72.9% ofsupervised mIoU. We demonstrate the effectiveness of FewIm-FT(Section 3.3) over varying values of k in Experiment II. k = 10
results in an improvement of 21.8% over SS-SFDA , which is
88.82% of supervised mIoU. Additionally, we show the benefit
of model distillation in Experiment III.

mance. Training with light fog improves the mIoU by
64.99% over the corresponding clear weather baseline. Initializing the model trained on light fog, and fine-tuning on
medium fog using SS-SFDA further improves the performance by 22.3%, thus resulting in a cumulative improvement of 101.84%. We do not show comparisons with supervised methods due to a lack of labeled datasets.

ExperimentmIoU

RecallPrec.

F1

CS pre-trained model
SS-SFDA
FewIm-FT, k=10

70.28
75.29
83.05

74.49
83.3
93.06

92.54
88.7
88.53

82.54
85.92
90.74

Table 9: Results on BDD: SS-SFDA improves baseline mIoU by
7.12%. Experiments on FewIm-FT (Section 3.3) demonstrate an
improvement of 10.30% over SS-SFDA for k=10, which is 93.11%
of supervised mIoU.
Experiment

MethodSource Data

mIoU%(sup)

85.96
86.12

93.87%
94.04%

78.64
84.77
86.67

85.87%
92.57%
94.64%

I. Synthetic Dataset: 100 mm rain
Domain Adaptation
Domain Adaptation

ADVENT [52]
PrDA [26]Self-training
SFDA
SFDA

CBST [65]
PrDA [26]
Ours

✓
✓

II. Heterogeneous Real Dataset: Berkeley Deep Drive
SFDA
SFDA

PrDA [26]
Ours

75.32
83.05

82.43%
93.11%

Table 10: Comparisons against the state-of-the-art: We adopt
the state-of-the-art methods in self-training, domain adaptation,and SFDA classification to road segmentation, and present the results on Rain 100mm. We pick the best performing source-freemodel and conduct experiments on BDD. We observe an improvement of 10.26%.

Analysis-Dark Zurich: The results are presented in Table
7. Table 7 (I) shows that the CityScapes pre-trained model
does not work well on Dark Zurich. SS-SFDA first trainson the twilight images, and then on the night images. Training with twilight images improves the mIoU by 24.91%
over the corresponding clear weather baseline. Initializing
the model with the model trained on twilight images, andfine-tuning on night images further improves performanceby 4.41%, thus resulting in a cumulative improvement of
31.47%. We do not show comparisons to supervised accuracies due to the unavailability of training labels.

4.4. Heterogeneous Real Datasets: Raincouver andBerkeley Deep Drive
Raincouver [51] and Berkeley Deep Drive (BDD) [58]are complex datasets with images containing a mix ofweather conditions in addition to scenes from different geographical regions. Raincouver consists of images captured
under rain during the night. BDD consists of images insnow, fog, low light, glare, rain, etc. We show that SSSFDA benefits from supervised finetuning with just 5 − 10
images using the procedure discussed in Section 3.3. The
models converge in 40 iterations, which takes 2 minutes totrain on one NVIDIA GeForce GPU with 11GB memory.
Analysis-Raincouver: The results are shown in Table 8.
SS-SFDA results in an mIoU of 52.42, which is 72.9% ofsupervised mIoU. In Table 8 (II), we show the effectivenessof the fine-tuning step. As k (number of supervised images used by the algorithm) increases, the performance ofthe model improves. Using just 1 image (k = 1) results in
an improvement of 4.3% (over SS-SFDA). k = 10 achieves
88.82% of supervised mIoU. In Table 8 (III), we demonstrate that model distillation improves mIoU by 11.86%.
Hyperparameter tuning on the model distillation hyperparameter λmodel-distil reveals that a value of 1.0 works best.

Analysis-Berkeley Deep Drive: The CityScapes model results in an mIoU of 70.28 (baseline). Training with SSSFDA improves performance by 7.12% over the baseline,
and is at 84.41% of supervised IoU. In Experiment III, wepick k random images from the dataset and finetune the network using the fine-tuning step. In concurrence with ourintuition, we observe that the performance improves as thenumber of images increases. For k = 10, we demonstrate
an mIoU improvement of 10.30% over SS-SFDA, which is
93.11% of supervised accuracy.

4.5. Training Time and Convergence
The pre-training step helps our model converges in 1/6
epoch (the accuracies are similar over multiple randomruns), thus bringing the training time down to 15 minutes on
an NVIDIA GeForce GPU with 11GB memory. Generative
SFDA models [28] take 30 epochs to converge, while priorwork on self-training [65] and SFDA [26] applied (where
feasible) to road segmentation converge in 3 epochs. Thus,
we improve training time over prior SFDA approaches by
18 − 180×.

4.6. Comparisons with Prior Work
We adapt (where feasible) the state-of-the-art methodin the following categories: self-supervised learning [65],source-free DA[26], and DA [52] to road segmentation
for comparisons. Other methods like feature alignmentby class-wise prototype learning [33], generative methods[28, 24, 29, 56, 31], and models that optimize source domain class priors [20] do not scale well to SFDA road segmentation due to the problems highlighted in Section 2.2.
We performed evaluations using each of the methods on
synthetic rain (Table 10 (I)) and observe that our model outperforms all prior methods. We finally train the best performing SOTA model on BDD (Table 10 (II)) for comparisons on heterogeneous real datasets which carry the highest
level of difficulty. On BDD our method outperforms priormethods by 10.26%.

5. Conclusion, Limitations, and Future Work
We propose a new method for road segmentation inadverse weather conditions using a novel self-supervised
source-free domain adaptation approach. Through our evaluations on 6 real and synthetic datasets, we show that that
our self-supervised model that has access to only a pretrained clear weather model and unlabeled target images exhibits accuracy that is comparable to completely supervised
models which have access to labels for all target domain
images. In addition, we exhibit benefits in terms of fastertraining time and state-of-the-art performance.
There are a few limitations of our work. Currently, ourapproach is designed for binary segmentation and cannot
perform multi-class segmentation. Extending the current
method to multi-class segmentation would generalize theapproach beyond road segmentation. Moreover, it wouldinteresting to investigate if SFDA via self-supervised learning could be extended to other computer vision problems
such as object recognition, image classification, scene understanding, etc.

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