Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
Abstract
In unsupervised domain adaptation (UDA), directly adapting from the source to the target domain usually suffers signiﬁcant discrepancies and leads to insufﬁcient alignment.
Thus, many UDA works attempt to vanish the domain gap gradually and softly via various intermediate spaces, dubbed domain bridging (DB).
However, for dense prediction tasks such as domain adaptive semantic segmentation (DASS), existing solutions have mostly relied on rough style transfer and how to elegantly bridge domains is still under-explored.
In this work, we resort to data mixing to establish a deliberated domain bridging (DDB) for DASS, through which the joint distributions of source and target domains are aligned and interacted with each in the intermediate space.
At the heart of DDB lies a dual-path domain bridging step for generating two intermediate domains using the coarse-wise and the ﬁne-wise data mixing techniques, alongside a cross-path knowledge distillation step for taking two complementary models trained on generated intermediate samples as ‘teachers’ to develop a superior ‘student’ in a multi-teacher distillation manner.
These two optimization steps work in an alternating way and reinforce each other to give rise to DDB with strong adaptation power.
Extensive experiments on adaptive segmentation tasks with different settings demonstrate that our DDB signiﬁcantly outperforms state-of-the-art methods.
Code is available at https: //github.com/xiaoachen98/DDB.git .
Introduction
When training deep models on one domain but applying it to other unseen domains, its performance typically drops seriously due to the domain shift/discrepancy issue [45, 44, 69, 55].
Since annotating data in the new scenario to re-train model to mitigate performance degradation is too expensive and time consuming, extensive researches have resorted to unsupervised domain adaptation (UDA) [41, 15, 34, 3], which aims to transfer knowledge from labeled source domain to unlabeled target domain.
Generally, existing UDA methods typically reduce the domain discrepancy by leveraging information statistics metrics [10, 27, 29, 33, 35, 46, 66] or adversarial training [15, 30, 34, 49, 54, 58, 3].
Both of these branches directly adapt the knowledge learned from the source domain to the target domain.
However, excessive/continuous domain discrepancies tend to limit the efﬁciency of these methods for knowledge transfer, causing non-optimal performance, especially on dense prediction tasks such as semantic segmentation.
[Table]
[Figure]
L o c a l r e p l a c e m e n t
To address this issue, some recent approaches tend to mitigate the excessive domain discrepancies by gradually transferring knowledge across domains by constructing intermediate domains in the input space [41, 60, 61], feature space [11, 12], or output space (self-training based) [64, 65].
Such mechanism of constructing intermediate domain is usually termed as domain bridging (DB).
Despite unprecedented advances achieved in UDA classiﬁcation task, the most common DB approaches, such as CycleGAN [72] and ColorTransfer [47] that based on style transfer, are inapplicable and cannot achieve satisfactory adaptation performance for the domain adaptive semantic segmentation (DASS).
These style transfer-based DB approaches are prone to generate unexpected artifacts in the global input space and also ignore to bridge in the label space, which makes the optimization constraints insufﬁcient for the dense prediction tasks such as DASS [16, 22, 67, 68, 71].
Therefore, such dilemma drives an urgent demand to investigate a new DB method for the densely predicted
DASS area.
In this paper, to consistently construct intermediate representations in the input space as well as the label space for domain bridging in DASS, we resort to the mix-based data augmentation techniques[63, 62, 18, 14, 43].
First, we study the existing data mix methods and group them into two categories according to their working way: global interpolation [63] and local replacement [62, 18, 14, 43, 71].
As shown in Fig.
1, the global interpolation based data mix may cause the pixel-wise ambiguity issue, but the local replacement based mix methods can better preserve the semantic integrity/consistency of objects for segmentation .
Next, to fully exploit the local replacement based data mix for domain bridging, we further deeply explore it from two complementary perspectives: the coarse region-level mix ( e . g ., CutMix [62], FMix [18]) and the ﬁne class-level mix ( e .
g ., ClassMix [43]).
We can also see from Fig.
1 that the coarse region-level domain bridging helps the model to exploit contextual information, reducing semantics confusion ( e .
g ., category confusion between objects such as ‘truck, bus, and train’).
Complementarily, the ﬁne class-level domain bridging enables the model to fully exploit the inherent properties of each category, making each object distinguishable.
However, these two groups of DB methods tend to drive the model to be overly dependent on contextual information or inherent properties, causing class bias and confusion in the target domain separately.
In this work, we propose a powerful DASS method called Deliberated Domain Bridging (DDB) to carefully take advantage of data mixing techniques and gradually transfer knowledge from the source domain to the target domain.
As an optimization strategy, DDB consists of two alternating steps, i .
e .
, Dual-Path Domain Bridging (DPDB) and Cross-path Knowledge Distillation (CKD).
In the ﬁrst step, DPDB independently leverages the coarse region-level data mix and ﬁne class-level data mix to construct two complementary bridging paths to train two expert teacher models, achieving dual-granularity domain bridging.
In the second step, CKD uses two complementary teacher models to guide one identical student model on the target domain, achieving adaptive segmentation.
These two optimization steps work in an alternating way, which allows the powerful teacher and student models to reinforce each other progressively based on the joint distributions of source and target domains.
The main contributions of this paper are summarized as follows:
• To the best
of our knowledge, this is the ﬁrst work that provides a comprehensive analysis w.r.t the recent domain bridging techniques when directly applied to the task of domain-adaptive semantic segmentation (DASS).
• Based on the analysis, we propose an effective DASS method called Deliberated Domain Bridging (DDB), which consists of two alternating steps – Dual-path Domain Bridging (DPDB) and Cross-path Knowledge Distillation (CKD).
These two optimization steps promote each other and progressively encourage two complementary teacher models and a superior student model (used for inference), achieving a win-win effect.
• We experimentally validate the superiority of our DDB not only in the single-source domain setting but also in the multi-source and multi-target domain settings and conclude that DDB outperforms previous methods tailored for each setting by a large margin.
Related Work
Domain Adaptive Semantic Segmentation (DASS).
This task aims to improve the adaptation performance for the semantic segmentation model to avoid laborious pixel-wise annotation in new target scenarios.
The recent DASS works can be mainly grouped into two categories: adversarial training based methods [23, 51–53, 37] and self-training based methods [24, 73, 70, 64, 65].
For the ﬁrst branch, most works tend to learn domain-invariant representations based on a min-max adversarial optimization game, where a feature extractor is trained to fool a domain discriminator and thus helps to obtain aligned feature distributions [51–53, 37].
The second branch focuses on how to generate highly reliable pseudo labels for the target domain data for further model optimization, which drives many classic related techniques, such as conﬁdence regularized pseudo label generation [73, 70] and category-aware pseudo label rectiﬁcation [64, 65].
These two branches of the DASS task both directly adapt the knowledge learned from the source domain to the target domain.
However, the large continuous domain discrepancies in DASS make such direct discrepancy minimization paradigms difﬁcult, due to the ﬁne-grained pixel-wise gap among different domains.
Domain Bridging (DB).
Instead of directly transferring knowledge from the source domain to the target domain, some UDA works in the tasks of classiﬁcation and person re-identiﬁcation tend to gradually transfer knowledge by building a bridge between source and target domains, i .
e ., constructing an intermediate domain on the image level [57, 41], on the feature level [11, 12], or on the output level [64, 65].
Representatively, GVB [11] designs a gradually vanishing bridge and inserts it into the task-speciﬁc classiﬁer and the domain discriminator to construct intermediate domaininvariant representations, reducing the knowledge transfer difﬁculty.
Along this road, some works [57, 60, 41, 12, 5, 50] resort to style transfer techniques [6, 22, 7, 16] and data mix techniques [63, 62, 43] for constructing various intermediate domains.
However, the existing DB approaches have not yet been extensively investigated in DASS.
In this paper, we ﬁrst perform a comprehensive analysis w.r.t the recent DB techniques and ﬁnd the complementarity between the coarse region-level DB and the ﬁne class-level DB methods, then deliberately/carefully apply these two DB methods to help the task of DASS.
Deliberated Domain Bridging 3.1 Recap of Preliminary Knowledge
For domain adaptive semantic segmentation (DASS), we denote the source domain as D s = { ( x ( i ) s , y ( i ) s ) }
N s i =1 with N s samples drawn from the source domain S , where x ( i ) s ∈
X s is an image, y ( i ) s ∈ Y s is the corresponding pixel-wise one-hot label covering K classes.
Similarly, the unlabeled target domain set is denoted as D t = { x ( i ) t } N t i =1 with N t samples drawn from the target domain T .
Note that the source and target domains share the same label space.
This work aims to learn a segmentation model for effectively transferring knowledge from the source domain to the target domain, ﬁnally achieving reliable pixel-wise predictions on the target data.
Following previous works [64, 65], this segmentation model M consists of a feature extractor that maps the image to the feature space and a classiﬁer that generates corresponding pixel-wise predictions.
3.2 Exploring Domain Bridging for DASS
Revisiting Existing DB Methods.
As mentioned in Sec. 2, the previous DB methods are mainly based on style transfer [72, 47], global interpolation based mix [63], and local replacement based mix [62, 18, 14, 43, 71].
Formally, the image-level style transfer-based DB methods can be formulated
[Table]
55.2 ± 1.
as,
[Equation]
where h ( · ) and h (cid:48) ( · ) represent the S → T translation function and T → S translation function, respectively.
Such style transfer-based DB approaches tend to generate unexpected artifacts at the image level and also ignore the inﬂuence of pixel-wise label correspondence.
In addition, we formalize the global interpolation mix based DB methods as,
[Equation]
where λ denotes the mixing ratio sampled from a beta distribution.
Furthermore, the local replacement mix based DB methods are formulated as,
[Equation]
where M denotes a binary mask indicating which pixel needs to be copied from the source domain and pasted to the target domain, 1 is a mask ﬁlled with ones, and (cid:12) represents the element-wise multiplication operation.
y t represents the pseudo label for target domain.
In particular, this local replacement DB contains two types of coarse region-level mix and ﬁne class-level mix.
As shown in Fig. 1(b), the binary mask M of the former is the cut patch [62, 18, 14] while M of the latter is obtained from the pixel-wise annotations in source domain [43].
Analyzing DB Methods with Toy Game.
From the above formalization, we can see that the global interpolation-based and local replacement-based DB methods both build bridges across the crossdomain joint distributions of input data, which can beneﬁt the densely predicted DASS task.
To verify this, we perform a toy game with a simple self-training based DASS pipeline following [50, 24] to evaluate the performance w.r.t semantic segmentation of different DB methods.
As illustrated in Tab.(a) and (b), although style transfer based and global interpolation based DB methods both outperform baseline ( i .
e ., the source only scheme), they are pronouncedly inferior to their local replacement-based counterparts.
This implies that for the DASS task, (1) it not only needs to construct an intermediate domain on the input space, but also the label space; (2) the local replacement based DB methods are more suitable for segmentation because they can better preserve the semantic integrity/consistency for pixels belonging to the same object.
In addition, Tab. 1 (b) shows that the performance of coarse region-level CutMix [62] and ﬁne class-level ClassMix [43] are comparable.
Thus, we further conduct a group of tests by combining different DB methods for a deeper study.
The results are shown in Tab.
1 (c), we can observe that (1) due to the unexpected artifacts, the segmentation performance is degraded when region-level DB methods are combined with style transfer ones; (2) we surprisingly notice that the coarse region-level and ﬁne class-level domain bridging methods can mutually reinforce/promote each other.
Analyzing the Local Replacement Based DB Methods with Visualization.
For the coarse regionlevel data mix methods ( e . g ., CutMix [62]), those pixels ( i .
e ., a patch) pasted to the target domain
[Figure]
usually have a rich contextual information.
For example, the pixel beneath a ‘person’ must belong to the ‘sidewalk’.
However, the area beneath a ’person’ is more likely to be ’road’ in the target domain.
Thus, such excessive hard reliance on context may introduce the issue of class bias from the source domain to the target domain, e .
g .
, ‘road’ and ‘sidewalk’ in Fig.
2 (c).
In contrast, the ﬁne class-level bridging method ( e . g ., ClassMix [43]) only pastes a set of pixels belonging to the same class to the target domain image, avoiding class bias issue, which also drives the model to discriminate different objects solely based on their inherent properties, leading to a more compact feature distribution in
Fig.
2 (d).
But, the scheme that only relies on the characteristics of each category may be confused by the classes that can easily be distinguished by the context, e .
g ., ‘train’ and ‘bus’ in Fig.
2 (d).
All in all, both coarse-grained and ﬁne-grained DB methods have their own advantages and drawbacks, and thus there is an urgent need to ﬁnd a appropriate way to combine them to achieve a win-win effect.
3.3 Progressively learning from Dual-grained Domain Bridging
Instead of directly combining individual DB methods as did in the bottom of Tab. 1(c), we propose an alternating optimization strategy to progressively transfer knowledge from the source domain to the target domain, which consists of two steps, i .
e .
, Dual-Path Domain Bridging (DPDB) and Cross-path
Knowledge Distillation (CKD).
These two steps are conducted iteratively, and the ending of each round will serve as the beginning for the next round (see detailed algorithm in supplemental material).
Dual-Path Domain Bridging (DPDB).
To better preserve and exploit the advantages of the coarse region-level and ﬁne class-level DB methods, we create bridging paths for them independently rather than simply fusing them.
Based on previous analysis and experiments, we utilize the cross-domain
CutMix [62] and ClassMix [43] techniques to construct the coarse region-path (CRP) and ﬁne classpath (FCP) domain bridging, respectively.
The self-training pipeline then proceeds in the following manner along each path (here we take the coarse region-path (CRP) as an example for illustration):
Following [2], to minimize the empirical risk on the unlabeled target domain, we simultaneously minimize the empirical risk on the source domain and mitigate the domain discrepancy.
The ﬁrst item is achieved with a pixel-wise cross-entropy (CE) loss,
[Equation]
where M C is the model training on the coarse region-path and H, W denote the sample height and width, respectively.
To mitigate the domain discrepancy, we minimize the CE loss on the constructed bridging path instead of adversarial training or minimize the predeﬁned discrepancy metric.
Considering the fact that the unlabeled target domain images are involved in the bridging path construction, an additional teacher network M (cid:48) C is employed to generate a denoised pixel-wise pseudo-label ˆ y t for x t through the exponential moving average (EMA) based on the weights of M C after each training step t ,
[Equation]
where α denotes the momentum and is set to 0 . 99 .
Based on Eqn. 3, we can generate the bridging image x crp and label ˆ y crp on the coarse region-path.
In line with [50, 43], a conﬁdence-based weight map m crp will be generated to regularize the target domain during the training process as follows,
[Equation]
where m t =
[Equation]
denotes the ratio of pixels that exceed a threshold τ on the maximum softmax probability and [ · ] represents the Iverson bracket.
Then, we minimize the CE loss on the region-path bridging,
[Equation]
Furthermore, the overall objective function of the self-training pipeline on the region-path bridging is summarized as,
[Equation]
Similarly, we can obtain the overall objective function L F on the ﬁne class-path (FCP) bridging.
By minimizing L C and L F separately for the coarse region-path and ﬁne class-path, we can obtain two complementary models.
The next problem that needs to be addressed is how to appropriately integrate these two kinds of complementary knowledge in an elegant manner.
Cross-path Knowledge Distillation (CKD).
Inspired by previous works [17, 21], knowledge can be transferred from a teacher network to a student network by knowledge distillation in the output space.
Here, we reform it to extract knowledge from two complementary teachers and adaptively transfer the integrated knowledge to a student.
Note that we only integrate and transfer the complementary knowledge in the unlabeled target domain.
Speciﬁcally, the outputs of two teachers have been adaptively weighted and ensembled as guidance to drive the student model to learn segmentation in the unlabeled target domain.
Furthermore, we experimentally choose the ‘hard’ distillation, i .
e ., ensembling the predicted softmax logits to generate a one-hot vector and utilizing the CE loss for supervising the student model M S .
The detailed distillation loss can be written as,
[Equation]
where x augt represents the target images augmented by color jitter and gaussian blur, and ¯ y t is obtained by weighted ensembling on the teachers’ softmax logits of the target image x t .
Intuitively, different samples, even different pixels in one sample, require different contributions from the two teacher models for ensembling.
We adaptively generate a pixel-wise weight map w ( i,j ) in the target domain by calculating the distance pixel-by-pixel between feature response f
( i ) before the classiﬁcation layer and the centroid η ( j ) of each category.
At each location, the closer the feature response is far from one centroid of certain category, the more likely it belongs to that category and thus should contribute more to the ensemble effect.
Therefore, taking the coarse region-path (CRP) as an example, we ﬁrst utilize the trained M C to calculate the centroid η ( j ) C of each category in the target domain,
[Equation]
Then, we deﬁne the adaptive ensemble weights w ( i,j ) C of M C as the softmax over feature distances to the centroids
[Equation]
Similarly, we can also obtain the ensembling weight of the other path w ( i,j ) F of M F .
Furthermore, we can obtain the pseudo-label ¯ y t , following the weighted ensembling,
[Equation]
where σ denotes the softmax function.
In addition, the student model is also supervised by the labeled source data to generate discriminative features
[Equation]
[Figure]
[Table]
The overall loss function of constraining the student model M S can be written as,
[Equation]
In the end
[Table]
a superior student model is well trained by adaptively integrating the knowledge from two complementary teacher models covering different granularities.
Alternating Optimization Strategy.
By integrating the complementary knowledge from two expert teacher models, we can obtain a superior student model.
In turn, this student model can be used to initialize the teacher models in the next new round, resulting in two stronger teacher models.
They promote each other, achieving a win-win effect.
Ultimately, we can obtain the most powerful student model after the ﬁnal round.
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r o a d s i d e w a l k bu il d
i ng w a ll f e n ce po l e li gh
t s i gn v e g e .
t
[Table]
rr a i n
[Table]
ky
[Table]
e r
[Table]
i
[Table] [Table]
Experiments 4.1 Experimental Settings
Datasets: We use four publicly available semantic segmentation benchmarks for validation, including two synthetic scenes and two real-world scenes.
Each scene has a unique structure and visual appearance.
In detail, GTA5 [48] is a synthetic dataset of 24,966 labeled images obtained from a video game.
Synscapes [56] is also a synthetic dataset of 25,000 images created by photo-realistic rendering techniques, and its style is closer to real-world driving scenes than GTA5.
Cityscapes [9] is a real-world urban dataset collected from European cities, with 2,975 images for training andimages for validation.
Mapillary Vista [42] is a large-scale dataset collected by various imaging devices worldwide and includes 18,000 images for training and 2,000 images for validation.
Implementation details: We use the mmsegmentation [8] codebase and train models on RTX 3090Ti
GPUs.
Following previous works [64, 65, 24, 59], we use the advanced DeepLab-v2 [4] model with
ResNet101 [20] pre-trained on ImageNet-1k [13] as backbone, and train the model with AdamW [36].
We set the learning rate as 6e-5 for the backbone and 6e-4 for the decoder head, use a weight decay of 0.01 and a linear learning rate warmup followed by 1.5k iterations linear decay.
All experiments are trained on a batch of 512x512 random cropped images for 40k iterations.
We set the batch size tofor analysis and experiments in Tab. 1 and Tab. 6, and set batch size to 4 for other results.
Following [50], we use the same augmentation parameters and set τ = 0 . 968 .
For CutMix [62], the ratio of the selected region for cross-domain pasting is experimentally set to 0.3.
For ClassMix [43], half of categories in the source domain are selected for cross-domain pasting.
4.2 Comparison with State-of-the-arts under Multiple Settings
GTA5 → Cityscapes (single-source).
Tab.
2 reports results on the validation set of Cityscapes.
Note that the comparable ProDA [64], UndoDA [32], and CPSL [28] have been improved with a warmup stage following existing DASS methods [51, 53].
Additionally, they also need to complete numerous rounds of self-distillation for the student model initialized by self-supervised pre-training.
Compared to these methods that require redundant optimization processes, the proposed DDB method requires only two alternating optimization steps of DPDB and CKD.
Despite simplicity, our method achieves a SOTA mIoU score of 62.7, outperforming existing methods signiﬁcantly, which also achieves the best IoU score in 7 out of 19 categories.
Particularly, thanks to combining the complementary teacher
[Table] [Table]
[Table] [Table]
[Table] [Table]
models devoted to exploiting the context and inherent properties by the coarse region-path and ﬁne class-path, the student model performs surprisingly well in those categories that are susceptible to the class bias issue, e .
g ., ‘sidewalk’ and ‘bike.’
Additionally, the ﬁnal student model also performs well in those categories suffering from semantic confusion, e .
g ., ‘person, rider’ and ‘truck, bus, train.’
GTA5 + Synscapes → Cityscapes (multi-source).
We also perform experiments under the multisource domain setting.
As shown in Tab. 3, our method obtains an impressive performance of 69.0 in mIoU, outperforming the previous SOTA methods over 10.0, achieving the best performance in all classes, especially those suffering class bias issue, e .
g ., ‘sidewalk’ and ‘bike.’
Although the proposed
DDB is not tailored for multi-source DASS, our method still beneﬁts this task by constructing intermediate domains between target and multiple source domains to facilitate knowledge transfer.
GTA5 → Cityscapes + Mapillary (multi-target).
Tab.
4 displays the performance of the proposed method in a multi-target domain setting.
Although the multi-target DASS is more challenging due to unknown distributions, our method can still achieve an impressive performance of 58.6 in mIoU on average for multiple target domains, outperforming the existing SOTA methods by a signiﬁcant margin.
Moreover, the proposed DDB outperforms ADAS [26] by 31.2 in averaged mIoU on the ‘sidewalk,’ by 14.1 in averaged mIoU on the ‘bus, train,’ which indicates our method avoids the class bias and confusion issues.
Such substantial performance gains comes from the DPDB-driven complementary teacher networks and the CKD-driven knowledge integration.
4.3 Ablation Study and Detailed Discussion
Complementarity Veriﬁcation.
To verify the complementarity of two teacher models trained on different bridging paths, we conduct an ablation where we train each path twice separately to obtain four different models.
After ensembling these models pair-by-pair, the segmentation results are presented in Tab. 6.
Unsurprisingly, the ensembled models across paths consistently perform better results than those only from a single view.
Study on Dual-path Domain Bridging.
In Tab. 5, the source-only model achieves 32.1 in mIoU on the target domain.
Combining the self-training pipeline with the coarse region-level and ﬁne class-level domain bridging, we can obtain two complementary teacher models, and they achieve 56.5 and 58.2 in mIoU, respectively.
As shown in Fig.
3, the coarse region-path tends to promote the model to utilize contextual information for prediction, whereas the ﬁne class-path enables the model to focus more on exploiting inherent properties.
Detailed results for the two teacher models in each category are provided in the supplemental materials .
Study on Cross-path Knowledge Distillation.
Since CKD is performed on the unlabeled target domain, as shown in Tab. 5, we use the more stable hard distillation, which performs 0.9 mIoU higher than the soft distillation using the Kullback-Leibler divergence.
Furthermore, our proposed adaptive ensemble scheme further improves the performance by 2.1 and 1.3 in mIoU in the case of soft and hard distillation, respectively.
After applying CKD equipped with the hard distillation and adaptive ensemble schemes, we can consistently obtain a superior student model.
Fig.
3 illustrates how the student model performs after integrating the knowledge from two complementary teacher models and alleviates the class bias and confusion issues in various domain settings.
Inﬂuence of Alternating Optimization Strategy.
As shown in Tab. 5, the alternation of DPDB and
CKD allows the complementary teacher and student models to promote each other and gradually transfer knowledge across domains.
We also test different alternating rounds on all three benchmarks, and report the performance of the student models after each round in Tab.
7 (more results are provided in the supplemental materials ).
The student model performs best across all three domain settings in the second round.
On the other hand, the student model shows a slight performance degradation after the third round of alternate training in the multi-source and multi-target domain settings.
We analyse the degradation is because the non-negligible domain conﬂict in these two settings.
Conclusions
In this paper, we study how the domain bridging techniques should be applied to domain adaptive semantic segmentation.
To ensure that the segmentation model takes full advantage of domain bridging while avoiding side effects, we propose an effective Deliberated Domain Bridging (DDB) method.
We build dual-path domain bridging (DPDB) with the coarse region-level data mix and ﬁne class-level data mix to construct two complementary teacher models.
Then, a superior student model can be generated from cross-path distillation (CKD) based on such two teacher models.
By alternating steps of DPDB and CKD, teacher models and student model would promote each other and progressively transfer knowledge from the source domain to the target domain.
Extensive ablation studies demonstrate the effectiveness of our method, and the experimental results on three benchmarks in the different settings further show its versatility and robustness.
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grant 61727809, in part by the Special Fund for Key Program of Science and Technology of Anhui Province under Grant 201903c08020002, and in part by the National Key Research and Development Program of China under Grant 2019YFC0117800.