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Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain Transfer

Recent advances in unsupervised domain adaptation have significantly improved the recognition accuracy of CNNs by alleviating the domain shift between (labeled) source and (unlabeled) target data distributions. While the problem of single-target domain adaptation (STDA) for object detection has recently received much attention, multi-target domain adaptation (MTDA) remains largely unexplored, despite its practical relevance in several real-world applications, such as multi-camera video surveillance. Compared to the STDA problem that may involve large domain shifts between complex source and target distributions, MTDA faces additional challenges, most notably the computational requirements and catastrophic forgetting of previously-learned targets, which can depend on the order of target adaptations. STDA for detection can be applied to MTDA by adapting one model per target, or one common model with a mixture of data from target domains. However, these approaches are either costly or inaccurate. The only state-of-art MTDA method specialized for detection learns targets incrementally, one target at a time, and mitigates the loss of knowledge by using a duplicated detection model for knowledge distillation, which is computationally expensive and does not scale well to many domains. In this paper, we introduce an efficient approach for incremental learning that generalizes well to multiple target domains. Our MTDA approach is more suitable for real-world applications since it allows updating the detection model incrementally, without storing data from previous-learned target domains, nor retraining when a new target domain becomes available. Our proposed method, MTDA-DTM, achieved the highest level of detection accuracy compared against state-of-the-art approaches on several MTDA detection benchmarks and Wildtrack, a benchmark for multi-camera pedestrian detection.


Introduction
With the advent of deep learning (DL) models such as Faster-CNN [1], object detection has experienced significant improvements. Despite the high level of accuracy provided by these models in a wide range of benchmark datasets and applications [2,3], current object detectors still suffer from poor generalization in the presence of domain shift between training and test datasets. For instance, in video surveillance applications, videos are captured over a distributed set of cameras with non-overlapping viewpoints.
The shift between the source (e.g., lab setting) and target (e.g., cameras) domains may lead to a significant decline in detection accuracy. A straightforward solution would involve a supervised fine-tuning of a given model using annotated target domain samples.
However, the high cost associated with the collection and annotation of target data may prohibit the adoption and deployment of such models in many practical scenarios.
Unsupervised Domain Adaptation (UDA) has been proposed to alleviate the problem of domain shift when labeled target data is not available. Depending on the number of target domains, UDA methods may either address single-target domain adaptation (STDA) or multi-target domain adaptation (MTDA) problems. STDA methods range 1 https://github.com/Natlem/M-HTCN from learning discriminant domain-invariant features using an adversarial loss [4,5], to learning mappings between a source and target domain for domain transfer [6,7], or to using well-known generative models, such as CycleGAN [8] in combination with previous methods for learning domain-invariant features. Existing STDA techniques can be extended to MTDA by either adapting multiple object detection models, one for each target domain ( Fig.1 (a)), or by treating multiple target domains as one target domain, and then applying STDA on a mixture of target data. Nevertheless, these approaches are complex because the number of models equals the number of target domains ( Fig.1 (a)), or generalize poorly on several distinct target domains, particularly when the number of target domains increases as shown in [9,10]. The poor performance of STDA in the MTDA setting, especially in object detection, is mainly due to the fact that generalizing on multiple target domains as a single domain is difficult since the underlying data distribution of these targets are still very different. This is why specific MTDA techniques need to be developed. In this way, the model can learn multiple underlying data distributions without sacrificing computation cost by using only one common multiple-target model. Despite its importance for many real-world applications, MTDA remains largely unexplored. For example, given a multi-camera detection problem in video surveillance, where videos are captured using several cameras with different viewpoints, backgrounds, and capture conditions (i.e., target domain), MTDA methods are required so that the object detector may generalize well to multiple target domains. Several techniques for MTDA have been proposed in the context of image classification [11,12]. Among these, AMEAN [11] assumes that there are no domain labels for targets and a common model can be optimized by minimizing the discrepancy between the source and clusters of pseudo sub-targets. This technique is, however, unsuitable for detection due to the lack of instances annotations for object clustering [11]. More recently, MT-MTDA [9] was proposed, which significantly outperforms [11] by training one specialized teacher per target domain and iteratively distilling each teacher to a common model ( Fig.1 (c)).
While [9] can be extended to detection by simply having one teacher detector per target and then apply feature distillation, it is unsuitable for real-world application due to the high computational requirements of [9], where one teacher detector per target domain is required. Furthermore, these methods are inflexible since they require access to previous target data when adapting for a new target domain. These limitations impede the deployment of these models in scenarios with limited storage or for privacy concerns on previous target domains.
Motivated by above issues with current MTDA strategies (i.e., low level of performance or high complexity), this paper focuses on low-cost incremental MTDA strategies for object detection tasks. Unsupervised incremental learning is leveraged with the goal of adapting a common model that can generalize well across multiple target domains without labels and without having to retrain on previously learned target data (see Fig. 1 (d)). To be the best of our knowledge, [10] is the only other method proposed to tackle incremental MTDA for the detection task. When adapting to a new target domain, it relies on knowledge distillation with a duplicated model trained on source data. This method distills knowledge from this duplicated to a common model, and constrains the output of the common model such that it does not deviate too much from the duplicated model. While this approach can alleviate knowledge corruption (i.e., catastrophic forgetting), its improvement in terms of detection accuracy remains limited, as it does not employ source-target feature alignment after adaptation, nor existing domain discriminators at different levels -image and instances (i.e., bounding box level) -of the detector. As shown in [13], the performance of this method may also improving accuracy since the approach proposed in [10] only uses source data to prevent catastrophic forgetting. Finally, our method allows for a considerable reduction in computational complexity when compared to [10] since DTM only needs 2 convolution layers as opposed to using a duplicated object detector to prevent catastrophic forgetting.
Target Source Target Source a) Source-Target feature alignment after adaptation b) Transferring Source to Target using DTM Figure 2: a) An illustration of the alignment of source (red circle) and target (blue cross) feature representations after domain adaptation. b) An illustration of the transfer of source feature (red circle) to pseudo-target feature representations (orange circle) that will be classified the same as target features (blue cross). Best viewed in color.
The main contributions of this paper are summarized as follows.
• An cost-effective method, referred to as MTDA with Domain Transfer Module (MTDA-DTM), which can adapt to novel target domains incrementally, while avoiding the problem of catastrophic forgetting.
• In particular, our approach integrates the new DTM, which transfers source images to a joint image space shared across previous target domains. This module allows for efficient training and adaptation compared to relevant state-of-the-art methods [10].
• The proposed MTDA-DTM is compared extensively against state-of-the-art MTDA techniques for object detection, consistently outperforming these methods across public MTDA detection benchmarks: PascalVOC-Clipart-Watercolor-Comic [16,17] and Cityscape-FoggyCityscape-RainCityscape [18,19,20]. Furthermore, we compared the proposed MTDA-DTM in a real-world video-surveillance application, addressing the problem of multi-camera person detection using the Wildtrack [21] dataset. Results show that our method brings a substantial improvement of up to 5%, in terms of accuracy, on the popular PascalVOC benchmark, and up to 2% on a real-world application dataset, i.e., Wildtrack.

Single-Target Domain Adaptation (STDA) for Object Detection:
While the literature for unsupervised domain adaptation in classification [5,22] or segmentation [23,24] is well explored, adaptation to the object detection task is not straightforward since domain adaptation for regression differs greatly from classification. This is mostly due to the difference between representation, and losses used by classification and regression. Several methods exist in the literature for this specific task [6,4,25]. In particular, authors in [4] propose to find domain-invariant features in both image and instances level using domain discriminators to encourage domain confusion through adversarial training. Most techniques have improved upon [4] by focusing on aligning local features such as color and texture [26]. [27] finds local region of interest for detection and aligns them. Recently, [28] applied domain adaptation on multiple layers of the feature extractor in addition to a weighted gradient reversal layer to handle hard confused domain samples. Using data augmentation, [29] proposes to apply single target domain adaptation by finding multi-domain invariant features of randomly augmented source and target domains. [30] proposes to evaluate the sample alignment using an uncertainty metric in order to apply conditional domain adaptation with curriculum learning. Similarly, [31] proposes to evaluate the "hardness" of samples in order to formulate an "easy-to-hard" adaptation, in addition to hierarchical feature alignment. Lastly, [25]

Multi-Target Domain Adaptation:
Current approaches for MTDA mainly focus on the classification task. These approaches can essentially be categorized into two groups: 1) methods using domain labels [32,9] as additional information or 2) approaches assuming domain labels are unavailable [11,9,12]. Nevertheless, as in the case of STDA techniques, MTDA approaches designed for classification are not suitable for object detection. AMEAN [11], one of the first techniques proposed for MTDA classification, assumes that no domain labels are available and tries to discover multiple hidden target domains using clustering on classification images. While AMEAN works for classification, it cannot be directly extended for object detection since, in this scenario, the object is among the background and there are not bounding box annotations, making the clustering of objects significantly difficult.
With the state-of-the-art MT-MTDA [9] technique for classification, one teacher classification model is used for each target domain with logits distillation for a common student. This technique can be employed with or without domain labels. While [9] can be extended to detection by simply having one teacher detector per target domain, and applying feature distillation instead of logits. However, this MTDA approach would be prohibitively costly for detection, since object detection models are already large by themselves. Furthermore, this approach is not scalable in the case of multiple target domains. Recently, another MTDA technique [33] has been also proposed for semantic segmentation using multiple teachers, where each teacher is responsible for a target domain, and distill to a single student. Although this method performs well on semantic segmentation tasks, it cannot be easily transferred to object detection as it uses specific features for the segmentation task, e.g. probability map for distillation, to improve performance. While, both these techniques achieve good generalization over multiple target domains, they lack flexibility to adapt the student model to new target domains, and require access to all target domains for learning.

Incremental Learning for Object Detection:
Incremental or continual learning refers to the ability of a model to learn new data while preserving previously-learned knowledge, without accessing all the previouslylearned data. This information can range from new classes, new tasks, or even new domains, such as in our case. In the current literature, there are three families of incremental learning. The first family of approaches is based on regularization using either weight-based [34,35] or, more recently, based on knowledge distillation [36,37], which have shown to achieve better performance. Additionally, both types of regularization can also be combined [38,39] to further improve the performance. The second type of approaches is based on memory replay where a fraction of data from previous tasks is retained. The manner in which the data is kept can range from directly storing raw data [40], using generative networks such as GAN or VAE to learn previous data distribution [41]. The third is based on architecture design [42,43] where the architecture of the CNN models can be adapted for new information. This can range from adding weights or even duplicated CNNs to an existing architecture.
While these techniques can perform well on supervised classification, they are ill-suited for tasks such as unsupervised detection due to the missing label information that is needed to select weights/samples or storing unsuitable information in detection.
For instance, DER [44], employs logits and even labels of certain samples to distill information from previous tasks. This is not possible in the context of object detection, where logits only help to mitigate catastrophic on the classification module and there is nothing to prevent the bounding box proposal module (RPN) to degrade on previouslylearned knowledge.
Even though a few attempts have been presented in few-shot detection [45], current literature in unsupervised incremental is very limited. The work of [46] handles the problem of incremental learning in object detection by introducing a replica of the detector trained on the previous task. This detector serves as a teacher and preserves knowledge of the previous task when the current detector learns a new task using distillation. [10] employs the approach in [46] in an unsupervised manner for MTDA by re-using features from the duplicated detector for domain confusion and a L2 distillation on outputs to avoid catastrophic forgetting. While [10] achieves good performance for MTDA, we argue that it does not take full advantage of the current UDA techniques and both these techniques require substantial resources. In addition, it only uses the source domain for distillation which has been shown in [13]

Multi-Task Learning for Object Detection:
Currently, most works on Multi-Task Learning (MTL) for object detection [47,48] seek to improve the performance of the main task (detection) instead of aiming at performing well across all tasks. In addition, these techniques assume either the datasets are labeled, or that they belong to the same domain, thus allowing the use of semisupervised techniques. This contrasts with our learning strategy, which does not make any of these assumptions. Recently, [49] proposes to tackle the problem of multi-domain detection. Their approach uses incremental learning by keeping samples from previous tasks to be used with distillation. This approach is not only computationally costly, but it also requires all task/target datasets to be labeled.

Preliminaries on Domain Adaptation for Object Detection:
Let us define a source labeled dataset S = {(X, y)}, where X i ∈ R H×W ×3 represents the i-th image, and H and W its spatial dimension. Furthermore, y i denotes the corresponding ground truth vector, which contains 5 elements, function r(·) is used to extract instance-level features using the region proposal network (RPN) as in [1], resulting into a vector p ∈ R V where V is an arbitrary size [1]. Last, a detector parameterized by Φ is used to provide the predicted vectorŷ from training samples in S. Furthermore, since our problem focus on unsupervised targets, we consider that Φ(X) also used to provide z and p from training samples in T i .
Currently, most state-of-the-art UDA techniques for object detection is based on [4], where domain-invariant features are found by encouraging domain confusion on both image-level (global feature map produced by feature extractor) and instance-level (feature map of bounding boxes) with losses: Where T 1 indicate (first) target domain and z and p are obtained from X using Φ. L C is a binary classification loss (e.g. focal loss [50] or standard cross-entropy) and the domain label d with d = 0 indicates source domain and d = 1 for target. D img and D inst are domain discriminators for the image-level and instance-level, respectively.
We note that [4] also uses a consistency loss between image and instances. However, recent works [6,26] have found that including this consistency term does not provide any improvement. Let us assume that a pre-trained detector is adapted to a target domain T 1 based on source data S, the overall domain adaptation loss for detection to update parameters of Φ and D img and D inst are characterized as: where L Sup is the supervised loss on labeled source S, defined as L Sup = L reg + L cls , with L reg the bounding box regression and L cls the object classification losses. Further detail can be found in [1]. Parameter λ controls the importance of domain adaptation losses. Recent works like [26] or [6] (used in our experiments) also have additional domain adaptation losses on different feature levels of the feature extractor. For the simplification of notation, we consider all domain discriminators (i.e. D img , D inst or others in [6]) as D C and all domain adaptation losses as L OD−DA since they only differ on whether its images or instances features. In addition, since bounding boxes and labels are only important for evaluation on source domain supervised training, we omit them in our notation:

Preliminaries on Incremental Learning:
For incremental learning to a new target T 2 , a basic approach involves applying Eq.
4 for unsupervised domain adaptation of the detection model, on a new target data T 2 : where Φ 1 and D C1 , are respectively, a detector and domain discriminators that are already adapted to previous target domains (T 1 ), and (T 2 ) is the new target to be learned.
While this simple approach can work because the source domain can serve as an anchor for all the targets, it may still skew the detector toward the latest target domain.
Indeed, using the source domain as an anchor only has a limited ability to prevent catastrophic forgetting since there are no explicit constraints on previous targets. To overcome this, we can retain the data of the previously-learned target domain, T 1 : However, it is prohibitively costly in many real-world detection applications to store and retrain on a mixture of data from all domains, especially with a growing number of target domains. In addition, incremental learning scenarios seek to learn new data without having access to previously-learned data.

MTDA with Domain Transfer Module:
With our proposed approach -MTDA-DTM, we perform unsupervised incremental learning of new target data using pseudo data of previous targets. This data is obtained via our DTM that is trained to trick the domain discriminators into classifying source-  In order to access previously-learned target domains, we rely on the DTM, which transfers source domain samples to a joint representation of target domains. The transfer is performed using two convolution layers and a ReLU layer in between. As shown in 3, the first convolution layer provides an expansion of the image channel-wise in order to project the image into a higher dimension space, and the second layer reduces the number of channels such that the output has the same number of channels as an image. This architecture was chosen since it is the simplest possible architecture, thus, allowing us to show the performance gain despite its simplicity. The optimization of DTM depends on how well pseudo-samples transferred by DTM from the source can force the domain discriminators to classify them as targets. We start by defining g(·) as the transformation learned by our DTM which is achieved by minimizing the binary classification loss with domain label d = 1 when samples are produced by DTM: where detector Φ i and domain discriminator D Ci are already adapted to target T i . Eq.7 where α represents the parameter to balance the importance of the loss on pseudosamples from DTM. Fig. 4 illustrates an incremental adaptation step to a new target domain T i of our detection model with a DTM trained using Eq. 7 with Φ i−1 . Compared to Eq.6, our optimization is more efficient since the DTM generates samples drawn from a joint representation of previously-learned targets, where there is less discrepancy between its samples compared to having different targets to optimize on as in Eq.6.
Algorithm 1 presents the overall training strategy.  Fig.5.
Foggy/Rain/Cityscape:. This ensemble of datasets contains three different datasets: Cityscape [18] as the source, and FoggyCityscape [19] and RainCityscape [20] [19] and [20], with several image samples depicted in Fig.5. In RainCityscape, we noticed a lack of samples for the class "train" in the evaluation subset. Thus, a set of 100 randomly selected images with class "train" was extracted out of 500 images in the training set and transferred to evaluation.
We provide this list of images on our repository for the community.

MTDA across cameras:
Wildtrack:. This scenario [21] is comprised of video data from seven different cameras made for supervised pedestrian detection and person re-identification. Each camera captured 400 frames at 1920 × 1080 resolution. This corresponds to a multi-camera DA scenario that's close to real-world applications. For our experiment on unsupervised IL, we will use Camera 1 (C1) as the source domain and all the other cameras (C2 − → C7) as target domains. Since the dataset is not provided with a standard split, a split of 2/3 (train) and 1/3 (test) was used for each camera to obtain a training set and an evaluation set, respectively. Fig.5 shows some image samples from Cameras (C1, C2, C4 and C7).

Implementation Details:
For our experiments, for PascalVOC and Cityscape, we use the same settings as in [6] and other papers [1,4] In all our experiments, we evaluate the accuracy of detection models according to the a mean average precision (mAP) with an IoU threshold = 0.5, where N C is the number of classes, and AP i is the average precision for class i: To compare the resource requirements of detection models, we show the number of parameters of a detector for memory complexity, and the number of floating-point operations per second (FLOPS) for time complexity. All our experiments were performed using an Nvidia Tesla-P100 GPU.

Results and Discussion
In this section, we first show the results of our method on MTDA benchmarks and then on the multi-camera problem. In a similar way to Incr. MTDA KD [10], results will 2 No public code was provided, thus we use our own implementation for experiments. be shown at each step and for each target on both scenarios of MTDA across datasets: [9] (adapted to detection) is also provide on the Cityscape scenario, since MT-MTDA [9] requires one teacher detection model per target, which makes it unusable for scenarios with multiple domains. Table 1 shows that our method performs slightly better than other baselines. We also noticed that "UFT" performs well for a naive solution. This suggests that existing STDA techniques are wellsuited for incremental learning because it learned domain-invariant features. However, we also observe that on "UFT" and method of Wei et al. [10]. "Incr. MTDA KD", the performance on previous target FoggyCityscape is lower than ours. This shows that our DTM is capable of generating samples that are close to previously-learned target of FoggyCityscape, thus our method has better abilities to limit the effects of catastrophic forgetting, therefore, increasing the accuracy. While the "Only DA" baseline has a better performance accuracy (it uses one detection model per target) because there are more parameters overall to optimize since each detector can specialize to a target, and it also requires significantly more resources. As for Mixed and UFT Prev, their performance is lower than MTDA-DTM due to the difficulty of directly generalizing on multiple target domains simultaneously. This difficulty of generalization is because target domains are far from each other which makes common domain-invariant features more difficult to find compared to finding domain-invariant for one target at a time.

MTDA across datasets:
Compared to supervised baselines, our method can achieve comparable or even better accuracy in several cases. Results suggest that it is easier to generalize to one target domain at a time. In addition, having multiple target domains may help improving performance since the results of "Only Sup." in RainCityscape is lower than other In the next scenario, the difference between our methods and unsupervised baselines is even more pronounced as the number of target domains grows.  also show the benefits of having multiple target domains. By comparing "UFT", Incr.
MTDA KD [10], MTDA-DTM, and "Only DA", results show that having multiple targets can lead to better performance than one detection model per target. Comparing with "UFT Prev." and "Mixed", results again confirm our analysis that generalizing directly on multiple targets can reduce performance. As for the supervised case (upper bounds), we confirm that the baselines with access to all domains, "Supervised Mixed," and that trains on one dataset, "Only Supervised," achieve higher performance thanks to class labels. However, performance degrades once there is fine-tuning to a new domain without having access to previously-learned domains in "Supervised FT". For the next incremental step, the Comic dataset is learned, and the effectiveness of our MTDA-DTM in the presence of multiple domains can be observed.  Table 4 confirms our results from previous tables and shows that our model outperforms comparable baselines and stateof-the-art, with performance gains of up to 2% in terms of accuracy. Compared to several ideal unsupervised baselines, such as "Only DA" --where one target is handled by one model-or "Mixed" --where all the target domains are mixed--, our method also provides higher accuracy. This shows that using a common model for all the target domains and adapt to one target at a time can improve performance. Nevertheless, although our MTDA-DTM has lower accuracy than "Supervised Mixed', it is expected since the latter has access to labels. Our model has very similar accuracy to "Supervised FT", mainly due to catastrophic forgetting in "Sup.FT". The performance degradation can be seen in the evolution of accuracy of Camera 2 at each incremental step in the   Figure 6. In addition, from the same figure, we again confirm that DTM provides better samples for optimization compared to using directly samples from previously-learned targets or using simultaneously samples of all the targets at once since MTDA-DTM mitigates catastrophic forgetting better than other methods.  From this figure, we observe that most methods tend to have a decrease in accuracy between the first adaptation and the last. In addition, we notice a performance drop for Incr. MTDA KD [10] at C3, which is due to its knowledge distillation although it managed to recover afterward thanks to learning from other domains. Comparing Incr.
MTDA KD [10] to "UFT" suggests that there's instability in the incremental learning process of Incr. MTDA KD [10] since "UFT" does not suffer catastrophic forgetting even without any module to prevent. This further indicates that the distillation of only source samples can eschew the feature representation and cause instability to the model. This problem is better mitigated by our algorithm as we can see that it managed to maintain a stable accuracy over all the incremental steps.

Ablation Studies:
Comparison with MT-MTDA. For this experiment, we adapted MT-MTDA [9], one of the current state-of-the-art in MTDA for image classification, using a feature-based [51] instead of logits-based distillation of the detection model.  Table 1. As for MT-MTDA, hyper-parameters that give the best results were chosen using a validation set. From Table 5, we observe that our model outperforms MT-MTDA [9] on all datasets of the Cityscape scenario. Results show that an incremental approach, where target domains are adapted one at a time, can be beneficial compared to an approach trying to directly generalize on several domains even, when it uses compressed knowledge.
To further confirm these results, we adapt our technique to image classification, and evaluate on PACS dataset. Table 6 shows that MTDA-DTM can outperform MT-MTDA in a classification setting by adapting to one model at a time, instead of simultaneously adapting to multiple domains.    Table 7 shows that our model from Pascal  Source-transferred clipart waterclor In Fig. 7, the pseudo-samples generated by DTM are projected back to image Based on this figure, DTM seems to focus on light intensity of the images, which can be the underlying common features between target domains. Results of Table 8 show that having an optimized DTM by using domain discriminators can achieve a significant increase in accuracy. In addition, "Not Trained" still has better accuracy than the other baselines. This is can be explained by the robustness of our detection model to open domain adaptation, as seen previously in Table 7. Fig. 8 shows an UMAP [52] visualization of features from all target domains and the pseudo-samples using the feature extractor and DTM from PascalVOC (S) − → Clipart (T 1 ) − → Watercolor (T 2 ) .
In this Figure, the features of samples from DTM, not only overlap with both previous target domains, which is our desired outcome, but they are also found outside of the distribution of previous targets, which can help improve accuracy. We employed UMAP [52] instead of TSNE [53] since it is better at preserving the global structure and it is also faster to compute.
Order of incremental learning. For this experiment, we evaluate the impact of the order of our incremental learning among the three target domains of Clipart (Cl), Watercolor (W), and Comic (Co) for the PascalVOC scenario and several pertinent target learning orders for Wildtrack scenario.  Table 9 shows that there can be a significant change in accuracy depending on which target domain we start with. These results suggest that starting with an easy target domain does not necessarily lead to better performance. A harder target(larger domain shift) seems to be preferred for our method to obtain a better accuracy for future target domains. This is potentially due to the fact that features learned from a harder     so much on the instances. In the Wildtrack problem, while the detection shift is still more pronounced albeit not as much as in Cityscape, the shift in classification is more pronounced. Finally, for the scenario of PascalVOC, we observe that there's not much change in confidence shift, this is confirmed by the results in Table 2 where the Source Only has similar performance compared to state-of-the-art like [10] or UFT baseline.

Complexity Analysis:
Object detectors can require a high computational complexity, especially region- This approach, however, introduces a significant overhead. Theoretically, a detector that uses C m amount of memory and C f amount of computation, for an incremental domain adaptation with a duplicated detection model it would take 2 × C M and 2 × C f during each incremental step. Using MT-MTDA [9], (state-of-the-art in image classification), has one teacher model per target domain, as well as one single student detector. Assuming that backbone of teachers and student is ResNet50, where n is the number of target domains. Then this approach requires n × C M and n × C f . In the following Table 12, we show the complexity of these methods, Incr. MTDA KD [10] and MT-MTDA [9] with an image RGB of size 3 × 1200 × 600 with 3 and 7 target domains. The complexity is measured over one iteration (one forward pass), for a whole training, the measure can be obtained by multiplying these measurements with the total number of iterations.  where detectors are supposed to excel given they have access to labels, it makes sense that our MTDA-DTM performs better than others given that target features are closer to the source and there are more target features overlapping with source features.

Conclusion
In this paper, a novel approach is introduced for efficient MTDA of object detectors that can achieve a high level of accuracy on multiple target domains without labels. The proposed approach, MTDA-DTM, is suitable for real-world applications since it allows the detector to be incrementally updated without degrading performance on data from  For this experiment, we used the Cityscape scenario and the PascalVOC − → Clipart − → Watercolor scenario as these two scenarios have used different α Table B.14 shows that the further we increase the value of α, the worse the performance drops, as seen in the case of α = 1.0. From the same Table B.14, we see that our training can be relatively stable. There is no significant difference between variations of α, the biggest difference is of 0.8 in mAP between α = 0.1 and α = 1 and the difference between two sequential values of α is mostly around 0.2, which means that our training is quite stable overall. From the Table B.15, we observe that our hypothesis about the value of α is confirmed. Our results increase when α is closer to 1 as this scenario has a bigger shift that Cityscape.

Appendix C. Architecture of DTM
In this experiment, we vary the architecture of DTM in order to evaluate its impact on performance. We explore 3 variations of DTM: 1) A wider DTM with 2 layers 2) A DTM with 4 layers 3) A DTM with 6 layers. These different architectures are described in Table C. 16.
From Table C.17, we observe that varying DTM's architecture only results in a slight change in performance. In this experiment, using a wider DTM lead to slight decreases