# Periodically Exchange Teacher-Student for Source-Free Object Detection

Qipeng Liu, Luojun Lin111Corresponding author, Zhifeng Shen, Zhifeng Yang
College of Computer and Data Science, Fuzhou University
<EMAIL_ADDRESS><EMAIL_ADDRESS>{shen_zhifeng<EMAIL_ADDRESS>
###### Abstract

Source-free object detection (SFOD) aims to adapt the source detector to
unlabeled target domain data in the absence of source domain data. Most SFOD
methods follow the same self-training paradigm using mean-teacher (MT)
framework where the student model is guided by only one single teacher model.
However, such paradigm can easily fall into a training instability problem
that when the teacher model collapses uncontrollably due to the domain shift,
the student model also suffers drastic performance degradation. To address
this issue, we propose the Periodically Exchange Teacher-Student (PETS)
method, a simple yet novel approach that introduces a multiple-teacher
framework consisting of a static teacher, a dynamic teacher, and a student
model. During the training phase, we periodically exchange the weights between
the static teacher and the student model. Then, we update the dynamic teacher
using the moving average of the student model that has already been exchanged
by the static teacher. In this way, the dynamic teacher can integrate
knowledge from past periods, effectively reducing error accumulation and
enabling a more stable training process within the MT-based framework.
Further, we develop a consensus mechanism to merge the predictions of two
teacher models to provide higher-quality pseudo labels for student model.
Extensive experiments on multiple SFOD benchmarks show that the proposed
method achieves state-of-the-art performance compared with other related
methods, demonstrating the effectiveness and superiority of our method on SFOD
task.

##  1 Introduction

Object detection has achieved significant progress with rapid development of
dataset scale and computation capability [31, 22, 4]. However, these detectors
are typically trained under an i.i.d assumption that the train and test data
are independently and identically distributed, which does not always hold in
real-world due to the existence of domain shift between the train and test
data. This can cause significant performance degradation when applying a model
well-trained on source domain (train data) to the target domain (test data).
Unsupervised domain adaptation (UDA), a recent research hotspot, can resolve
this dilemma by enabling the model to adapt effectively to the target domain.
This is achieved through joint training, leveraging both labeled source domain
data and unlabeled target domain data to enhance the model’s performance in
the target domain.

Figure 1: The training paradigms of the _mean-teacher_ and the proposed
_periodically exchange teacher-student_ method. T and S denote the teacher
model and student model, respectively. ST represents the static teacher with
fixed weights in each period, and DT is the dynamic teacher updated by the EMA
of the student models. $t_{i}$ represents the $i$-th period in whole training
stage.

There are many UDA methods developed to address domain shift in image
classification tasks [27, 35, 37, 46]. However, these methods cannot meet the
growing demand for data privacy protection. Moreover, directly applying these
UDA methods to object detection tasks cannot achieve satisfactory performance.
In light of the above considerations, source-free object detection (SFOD) has
rapidly emerged as an urgent task to attract the attention of researchers. The
purpose of SFOD is to achieve effective adaptation of a detector, originally
trained on a labeled source domain, to the unlabeled target domain, without
accessing any source data during adaptation. Compared with source-free image
classification, SFOD is a more challenging task that not only requires
regression, i.e., locating the bounding box of each object, but also involves
classification, i.e., identifying the associated class of each object in
diversely-scaled images.

(a) EMA weight = 0.99

(b) EMA weight = 0.999

(c) EMA weight = 0.9996

(d) EMA stepsize = 600

(e) EMA stepsize = 1500

(f) EMA stepsize = 3000

Figure 2: The training curves of different SFOD methods (i.e. conventional MT
and IRG [39]) with different EMA hyper-parameters on C2F benchmark [33]. These
methods show a consistent phenomenon: when the performance of the teacher
model crashes, the student model always follows the downward trend of the
teacher model even with different EMA weights or stepsizes.

Most of the existing SFOD studies [24, 43, 25, 8, 40] are based on self-
training paradigm using a mean-teacher (MT) framework [36] along with other
improved UDA techniques. These MT-based methods involves using a single
teacher model to guide the student model, where the teacher model is an
exponential moving average (EMA) of the student model at different time steps,
and the student model is updated based on the pseudo labels provided by the
teacher model. The MT framework assumes that _the teacher model can be
improved continuously as the training progresses, and the student model can
gradually approach the performance of the teacher model_. However, since the
source-pretrained model introduces inherent biases when applied to the target
domain, the teacher model, as an EMA of the student model inherited from the
source-pretrained model, is susceptible to accumulating errors from the
student model. This error accumulation leads to a concerning issue of
_training instability_ for the teacher model, thereby making the initial
assumption no longer holds true. That is, when the single teacher model makes
mistakes, the student model tends to replicate the errors without any
correction measures. It finally leads to uncontrollable degradation of the
detection performance for MT-based SFOD methods.

In order to mitigate the training instability problem, a natural solution
involves adjusting the EMA hyper-parameters to encourage a more gradual and
stable evolution of the teacher model. For example, the recent works [39, 8]
have explored the strategy of employing a larger EMA update stepsize, with the
aim of slowing down the updating process of the teacher model. Another line of
exploration in this direction involves assigning a higher EMA weight to the
historical teacher model, amplifying the influence of the past iterations and
consequently reducing the updating rate of the teacher model. However, these
efforts have yielded limited success. As shown in Figure 2, the efforts to
enhance the EMA weights or increase the EMA update stepsize do not completely
resolve the issue of training instability problem within the MT-based
frameworks. Besides, it is inconvenient to search for an optimal EMA hyper-
parameter to properly update the teacher model.

In this paper, we aim to address the instability problem and thus propose a
simple yet novel _Periodically Exchange Teacher-Student (PETS)_ method to
improve the self-training paradigm of the MT framework. As shown in Figure 1,
our method is a multiple-teacher framework consisting of a static teacher
model, a dynamic teacher model, and a student model. Unlike the previous
methods that keep the roles of student and teacher unchanged throughout the
training, we periodically exchange the positions between the student model and
the static teacher model. Then, the static teacher model freezes its weights
until the next exchanging period; while the student model is trained using the
supervision signals provided by the two teacher models, and the dynamic
teacher model is updated by an EMA of the student per iteration within each
period. In this way, the dynamic teacher implicitly reduces error accumulation
to improve its performance. Moreover, the exchange between the static teacher
and student helps to prevent a rapid decrease in the lower bound of the
student model, ultimately improving the robustness of whole models in our
method. Besides, we also propose a consensus mechanism to merge the
predictions from the static and dynamic teachers, which can provide higher-
quality pseudo labels to supervise the student model.

Our method is evaluated on four SFOD benchmarks. The experimental results show
that our method achieves competitive results compared with existing SFOD
methods, and demonstrate its effectiveness to solve the instability problem of
current MT-based frameworks. The main contributions of our method are
summarized as follows:

  * •

We highlight the _training instability_ issue within the MT framework, where
the errors from the teacher model can be replicated by the student model
without correction measures. This will result in an uncontrollable degradation
of detection performance in MT-based SFOD methods.

  * •

We propose a simple yet novel _Periodically Exchange Teacher-Student (PETS)_
method to address the training instability issue for MT framework. Our method
consists of a static teacher, a dynamic teacher and a student model. At the
end of each period of training, we exchange the weights between the student
and the static teacher to reduce error accumulation. Within each period, we
train the student model through the two teacher models, and update the dynamic
teacher with an EMA of the student model per iteration.

  * •

We design a consensus mechanism to integrate the predictions from the static
teacher and the dynamic teacher models. It integrates knowledge from
historical iterations to prevent catastrophic forgetting, which can achieve
higher-quality pseudo labels to supervise the student model.

  * •

Extensive experiments on multiple SFOD benchmarks show that the proposed
method achieves state-of-the-art performance compared with other related
methods, demonstrating the effectiveness and superiority of our method on SFOD
task.

##  2 Ralated Works

###  2.1 Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a source
domain with labeled data to a target domain without labeled data. The current
UDA methods can be roughly categorized into three types: domain translation,
adversarial learning and pseudo labeling. The domain translation methods aim
to transform a target image into a source-like image by using statistic
information in the model [45, 15] or employing a translation network [20, 10,
41]. Adversarial learning is also frequently adopted in UDA tasks by employing
a domain discriminator [11] or designing adversarial loss functions, in order
to narrow the gap between source and target domains in feature space [3, 37,
23, 29]. Unlike previous methods, pseudo labeling, as one of the most popular
self-training paradigms [6], has been an effective approach for UDA, which is
mainly constructed based on the mean-teacher (MT) framework [36] that exploits
the pseudo labels provided by the teacher model to supervise the student
model. Most pseudo labeling methods concentrate on designing interaction
manners between the student and teacher models [19, 5, 51]. In this paper, we
concentrate on source-free object detection and try to improve self-training
paradigm for MT-based SFOD framework.

###  2.2 Source-Free Object Detection

Several UDA approaches have been applied to _Unsupervised Domain Adaptive
Object Detection (UDAOD)_ , which can also be categorized into adversarial
learning [7, 32, 13], domain translation [16, 20] and pseudo labeling [1, 10].
Given that these methods have been introduced briefly in previous section, we
only discuss the final one since our work is constructed on the basis of self-
training. To obtain more accurate pseudo labels, UMT [10] transforms target
domain data into source-like data in order to improve the quality of generated
pseudo-labels. SimROD [30] enhances the teacher model by augmenting its
capacity for generating higher-quality pseudo boxes.

With the urgent need for data privacy protection, _Source-Free Object
Detection (SFOD)_ has emerged as a new branch of UDAOD in recent years. Due to
the complexity of the object detection task (numerous regions, multi-scale
features, and complex network structure) and the challenge of the absent
source data, simply applying the existing UDA-Classification or UDAOD methods
to SFOD tasks can not get satisfied results [48, 26]. Therefore, SFOD [25]
develops a novel framework that uses self-entropy descent to select high-
quality pseudo labels for self-training. SOAP [43] devises domain perturbation
on the target data to help the model learn domain-invariant features that are
invariant to the perturbations. LODS [24] proposes a style enhancement module
and graph alignment constraint to help the model learn domain-independent
features. A2SFOD [8] divides target images into source-similar and source-
dissimilar images and then adopts adversarial alignment between the teacher
and student models. IRG [39] designs an instance relation graph network
combined with contrastive loss to guide the contrastive representation
learning. While the majority of these approaches rely on the MT framework
[28], they tend to overlook the issue of training instability arising from a
single teacher model. This oversight allows errors to be replicated by the
student model, consequently constraining its performance. To tackle this
concern, we propose a _Periodically Exchange Teacher-Student_ approach that
leverages knowledge from historical models to prevent catastrophic forgetting
for MT framework.

Figure 3: The training pipeline of the proposed _Periodically Exchange
Teacher-Student_ method, which can be divided into two parts: (a) Outer-period
exchange of teacher-student: exchange the weights between the student and
static teacher after each period; (b) Inner-period training with consensus
mechanism: update the dynamic teacher with an EMA of the student model, and
train the student model with a consensus mechanism that fusions the
predictions from multiple teachers.

##  3 Preliminary

Let $\mathcal{D}_{S}=(\mathcal{X}_{S},\mathcal{Y}_{S})$ represent the labeled
data in the source domain, and $\mathcal{D}_{T}=(\mathcal{X}_{T})$ denote the
unlabeled data in the target domain, where
$\mathcal{X}_{S}=\\{x_{s}^{i}\\}^{N_{S}}_{i=1}$ represents the image set of
the source domain, $\mathcal{Y}_{S}=\\{y_{s}^{i}\\}^{N_{S}}_{i=1}$ represents
the corresponding label set containing object locations and category
assignments for each image, and
$\mathcal{X}_{T}=\\{x_{t}^{i}\\}^{N_{T}}_{i=1}$ denotes the image set of the
unlabeled target domain. $N_{s}$ and $N_{t}$ correspond to the number of
labeled source data and unlabeled target data, respectively.

In the setting of SFOD task, a source pre-trained model, denoted as
$f_{S}:\mathcal{X}_{S}\rightarrow\mathcal{Y}_{S}$, is initially available to
perform adaptation on unlabeled target domain. However, due to the inherent
domain gap between the source and target domains, the mapping $f_{S}$
diminishes performance when directly applied to the target domain.
Consequently, the primary objective of SFOD is to acquire a new mapping
$f_{T}:\mathcal{X}_{T}\rightarrow\mathcal{Y}_{T}$ by leveraging the source-
pretrained model $f_{S}$ in conjunction with the unlabeled target data
$\mathcal{X}_{T}$ without accessing any source data.

Most previous SFOD methods use Faster-RCNN [31] as their backbone network. To
ensure a fair comparison with previous methods, we also adopt Faster-RCNN as
the backbone network here. Therefore, the training goal of $f_{T}$ is similar
to Faster-RCNN, which can be written as:

$\mathcal{L}_{det}=\mathcal{L}_{cls}^{RPN}+\mathcal{L}_{reg}^{RPN}+\mathcal{L}_{cls}^{ROI}+\mathcal{L}_{reg}^{ROI},$
(1)

where $\mathcal{L}_{cls}^{RPN}$ and $\mathcal{L}_{reg}^{RPN}$ represent the
losses of foreground prediction and box location from the RPN network,
respectively. $\mathcal{L}_{cls}^{ROI}$ and $\mathcal{L}_{reg}^{ROI}$ are the
losses of category prediction and box location from the ROI head,
respectively.

##  4 Methodology

###  4.1 Overview

Our method involves using a static teacher model, a dynamic teacher model, and
a student model. The pseudo code of training process can be seen in Algorithm
1. Figure 3 shows the training pipeline of our method, which can be divided
into two parts:

  * 1)

Outer-period exchange of teacher-student: After each period of training, we
exchange the weights between the student model and the static teacher model.
In other words, the static teacher and the student reverse their roles per
period, as shown in Figure 3(a). Note that the term “period” is synonymous
with the concept of an epoch during training.

  * 2)

Inner-period training with consensus mechanism: The weights of static teacher
model are fixed within each period. The dynamic teacher is updated by the EMA
of the student model in each iteration, and the student model is supervised by
the pseudo labels merged from the dynamic and static teacher models with
consensus mechanism, as shown in Figure 3(b).

##### Notations

For better understanding our method, we use $\Theta_{S}$, $\Theta_{ST}$ and
$\Theta_{DT}$ to denote the student model, the static teacher model and the
dynamic teacher model, respectively.

Algorithm 1 Python-like code of training process

⬇

# Outer-period exchange of teacher-student

if epoch % time_period == 0:

exchange_weight(student, static_teacher)

# Inner-period training with consensus mechanism

for _, images in enumerate(loader):

# images: [N, C, H, W]

# N: number of images per mini-batch

# pre-process images by data augmentation

img_w = weak_aug(images)

img_s = strong_aug(img_w)

# obtain predictions

pred_s = student(img_s)

pred_st = static_teacher(img_w)

pred_dt = dynamic_teacher(img_w)

# produce pseudo label

pseudo_labels = consensus(pred_st, pred_dt)

# compute detection loss

loss = compute_loss(pred_s, pseudo_labels)

# update the student by back-propagation

loss.backward()

# update the dynamic teacher by EMA

update_teacher(student, dynamic_teacher)

###  4.2 Outer-period Exchange of Teacher-Student

The training process can be divided into multiple independent time periods
(i.e., epochs). Each period is represented as $t$. At the $2t+2$ period, the
weights of the student model are swapped by that of the static teacher model
at the $2t+1$ period. Conversely, the weights of the static teacher model at
the $2t+2$ period are exchanged by that of the student model at previous
period. The exchange process can be written as:

$\displaystyle\Theta_{S}^{2t+1}\longrightarrow\Theta_{ST}^{2t+2},\quad\Theta_{ST}^{2t+1}\longrightarrow\Theta_{S}^{2t+2},$
(2)

where $\Theta_{ST}^{2t+2}$ and $\Theta_{S}^{2t+2}$ denote the static teacher
model and the student model at the $2t+2$ period, respectively. This exchange
strategy keeps periodically recycling during the whole training process.

The exchange strategy benefits each model from the following perspectives: 1)
Student model: The static teacher model serves as a performance lower bound
for the student model. If the student model crashes into a collapse issue
guided by the declined dynamic teacher, the exchange can ensure that the
student model reverts to previous period, effectively mitigating its downward
trend. In essence, the exchange helps prevent a rapid decrease in the
performance lower bound of the student model, thus improving its robustness.
2) Static teacher model: The exchange strategy ensures periodic updating to
the static teacher model’s knowledge, which is executed at a notably slow rate
to enable a more stable model. 3) Dynamic teacher model: The dynamic teacher
model is a temporal ensemble of the student model exchanged by the past
student model. In practice, the updating rate of the dynamic teacher model is
implicitly reduced. Thus, it has a better ability to resist noise compared to
the conventional mean-teacher framework [36]. In summary, our _periodically
exchange teacher-student_ strategy can enable the student and teacher models
to mutually prevent catastrophic forgetting and uncontrollable collapse, thus
improving the detection performance.

###  4.3 Inner-period Training with Consensus Mechanism

During each period, the static teacher maintains fixed weights until iterating
to next period. Simultaneously, the dynamic teacher model is updated by the
temporal ensembling of the student model, and the student model is updated by
pseudo labels as supervision signals, where the pseudo labels are generated by
combining the predictions of the dynamic and static teachers through the
consensus mechanism. This procedure is illustrated in Figure 3(b). The
following sections delve into the details of the consensus mechanism, the
learning process of the student model, and the updating of the dynamic teacher
model.

####  4.3.1 Consensus Mechanism

Our framework incorporates two distinct teachers: the static teacher and the
dynamic teacher. A notable advantage of our approach is the ability to
leverage predictions from both teachers to enhance the quality of pseudo
labels. To this end, we design a consensus mechanism that includes two main
steps: filtering and fusion.

##### Filtering

Since the output of teacher models contains inevitable noise (low-confidence
predictions), we set a category confidence threshold $\delta=0.5$ to pre-
filter low-confidence predictions. This can prevent the subsequent fusion
process suffering from the interference of noisy labels.

##### Fusion

For a weakly-augmented target image $x_{t}\in\mathcal{X}_{T}$, the predictions
of the static teacher and dynamic teacher are represented as
$Y_{ST}=\\{(b_{ST}^{i},c_{ST}^{i},y_{ST}^{i})\\}^{n}_{i=0}$ and
$Y_{DT}=\\{(b_{DT}^{j},c_{DT}^{j},y_{DT}^{j})\\}^{m}_{j=0}$, where $b,c,y$
represent the bounding box coordinates, classification confidence and category
label of each predicted object, and $n,m$ denote the number of predicted
objects of the static teacher and the dynamic teacher, respectively. Then, we
select the objects with identical category and a higher intersection over
union (IOU) between the predicted boxes of the static teacher and the dynamic
teacher. The selection criterion can be represented as
$IOU(b_{ST}^{i},b_{DT}^{j})\geq\eta\And y_{ST}^{i}=y_{DT}^{j}$, where $\eta$
is the threshold of judging whether the predicted box belongs to the same
object. We usually set $\eta=0.5$. Lastly, we employ the weighted boxes fusion
(WBF) strategy [34] to merge the selected boxes derived from both the static
teacher and dynamic teacher models. The process can be formulated as:

$\displaystyle\widetilde{b}=\frac{1}{C}(\sum_{i=1}^{N}c_{ST}^{i}*b_{ST}^{i}+\sum_{j=1}^{M}c_{DT}^{j}*b_{DT}^{j}),$
(3)
$\displaystyle\widetilde{c}=\frac{\beta}{N}\sum_{i=1}^{N}c_{ST}^{i}+\frac{1-\beta}{M}\sum_{j=1}^{M}c_{DT}^{j},$

where $N,M$ are the number of boxes belonging to the same object predicted by
the static teacher and the dynamic teacher, respectively. $C$ is the sum of
$\sum_{i=1}^{N}c_{ST}^{i}$ and $\sum_{j=1}^{M}c_{DT}^{j}$. $\beta$ controls
the fusion magnitude between the static teacher and dynamic teacher, which is
ranged in $[0,1]$ and set to $0.5$ in this paper. We ultimately obtain pseudo
label $\widetilde{Y}=\\{(\widetilde{b},\widetilde{c},\widetilde{y})\\}$ for
the unlabeled target image $x_{t}$, where $\widetilde{b}$ and $\widetilde{c}$
denote the coordinates and confidence of the fused bounding box, respectively,
and $\widetilde{y}$ is equivalent to $y_{ST}^{i}$. The fused pseudo labels
exhibit greater resistance to confirmation bias compared to those single-
teacher framework.

####  4.3.2 Student Learning

Given an unlabeled target image $x_{t}$, its pseudo label can be represented
as $\widetilde{Y}=\\{(\widetilde{b},\widetilde{y})\\}$ that can be used as the
supervision signal of the student model. Following Equation 1, the training
loss of the student model $\Theta_{S}$ can be defined as:

$\displaystyle\mathcal{L}_{s\\_{det}}=\sum\limits_{\bar{x}_{t}\in\mathcal{X}_{T}}$
$\displaystyle\mathcal{L}_{cls}^{RPN}(\Theta_{S}(\bar{x}_{t}),\widetilde{y})+\mathcal{L}_{reg}^{RPN}(\Theta_{S}(\bar{x}_{t}),\widetilde{b})+$
(4)
$\displaystyle\mathcal{L}_{cls}^{ROI}(\Theta_{S}(\bar{x}_{t}),\widetilde{y})+\mathcal{L}_{reg}^{ROI}(\Theta_{S}(\bar{x}_{t}),\widetilde{b}),$

where $\bar{x}_{t}$ denotes the strongly-augmented version of the target image
$x_{t}$. Since the proposed consensus mechanism can provide more precise
bounding boxes compared with previous studies [10], we use both the category
prediction loss and box location loss to train the student model.

####  4.3.3 Dynamic Teacher Updating

Throughout each period, the static teacher model maintains fixed weights
across various iterations, whereas the dynamic teacher model adjusts its
weights in each iteration. We follow the conventional MT framework that uses
the exponential moving average (EMA) strategy to update the dynamic teacher
model $\Theta_{DT}$. This can be formulated as:

$\Theta_{DT}\leftarrow\alpha\Theta_{DT}^{\prime}+(1-\alpha)\Theta_{S},$ (5)

where $\Theta_{DT}$ represents the dynamic teacher in current iteration, while
$\Theta_{DT}^{\prime}$ pertains to the dynamic teacher in previous iteration.
The hyper-parameter $\alpha$ controls the update rate of the dynamic teacher,
with a higher value leading to a slower update rate. In this study, we
empirically set $\alpha$ to 0.999.

##  5 Experiments

We conduct comprehensive experiments to evaluate the effectiveness of our
method on multiple standard SFOD benchmarks. Then, we perform ablation studies
by using different exchange strategies to stress the effectiveness of the
proposed periodic exchange strategy. Finally, we analyze the promising results
of our method through detailed visualization and component analysis.

Methods | Person | Rider | Car | Truck | Bus | Train | Motor | Bicycle | mAP
---|---|---|---|---|---|---|---|---|---
| Source only (Single level) | 23.4 | 23.8 | 29.7 | 8.1 | 12.9 | 5.0 | 18.3 | 24.5 | 18.2
| Source only (All levels) | 35.1 | 39.4 | 47.0 | 10.7 | 32.5 | 10.1 | 30.0 | 36.9 | 30.7
UDAOD | MAF [13] | 28.2 | 39.5 | 43.9 | 23.8 | 39.9 | 33.3 | 29.2 | 33.9 | 34.0
SW-Faster [32] | 32.3 | 42.2 | 47.3 | 23.7 | 41.3 | 27.8 | 28.3 | 35.4 | 34.8
iFAN [52] | 32.6 | 40.0 | 48.5 | 27.9 | 45.5 | 31.7 | 22.8 | 33.0 | 35.3
CR-DA-DET [44] | 32.9 | 43.8 | 49.2 | 27.2 | 45.1 | 36.4 | 30.3 | 34.6 | 37.4
AT-Faster [14] | 34.6 | 47.0 | 50.0 | 23.7 | 43.3 | 38.7 | 33.4 | 38.8 | 38.7
SFOD | SED(Mosaic) [25] | 33.2 | 40.7 | 44.5 | 25.5 | 39.0 | 22.2 | 28.4 | 34.1 | 33.5
HCL [17] | 26.9 | 46.0 | 41.3 | 33.0 | 25.0 | 28.1 | 35.9 | 40.7 | 34.6
$\text{A}^{2}$SFOD [8] | 32.3 | 44.1 | 44.6 | 28.1 | 34.3 | 29.0 | 31.8 | 38.9 | 35.4
SOAP [43] | 35.9 | 45.0 | 48.4 | 23.9 | 37.2 | 24.3 | 31.8 | 37.9 | 35.5
LODS [24] | 34.0 | 45.7 | 48.8 | 27.3 | 39.7 | 19.6 | 33.2 | 37.8 | 35.8
IRG [39] | 37.4 | 45.2 | 51.9 | 24.4 | 39.6 | 25.2 | 31.5 | 41.6 | 37.1
Ours (Single level) | 42.0 | 48.7 | 56.3 | 19.3 | 39.3 | 5.5 | 34.2 | 41.6 | 35.9
| Ours (All levels) | 46.1 | 52.8 | 63.4 | 21.8 | 46.7 | 5.5 | 37.4 | 48.4 | 40.3
| Oracle | 51.3 | 57.5 | 70.2 | 30.9 | 60.5 | 26.9 | 40.0 | 50.4 | 48.5

Table 1: Results of adaptation from normal to foggy weather (C2F). “Source
only” and “Oracle” refer to the models trained by only using labeled source
domain data and labeled target domain data, respectively.

Methods | Truck | Car | Rider | Person | Motor | Bicycle | Bus | mAP
---|---|---|---|---|---|---|---|---
| Source only | 9.9 | 51.5 | 17.8 | 28.7 | 7.5 | 10.8 | 7.6 | 19.1
UDAOD | DA-Faster [7] | 14.3 | 44.6 | 26.5 | 29.4 | 15.8 | 20.6 | 16.8 | 24.0
SW-Faster [32] | 15.2 | 45.7 | 29.5 | 30.2 | 17.1 | 21.2 | 18.4 | 25.3
CR-DA-DET [44] | 19.5 | 46.3 | 31.3 | 31.4 | 17.3 | 23.8 | 18.9 | 26.9
SFOD | SED [25] | 20.4 | 48.8 | 32.4 | 31.0 | 15.0 | 24.3 | 21.3 | 27.6
SED(Mosaic) [25] | 20.6 | 50.4 | 32.6 | 32.4 | 18.9 | 25.0 | 23.4 | 29.0
$\text{A}^{2}$SFOD [8] | 26.6 | 50.2 | 36.3 | 33.2 | 22.5 | 28.2 | 24.4 | 31.6
Ours | 19.3 | 62.4 | 34.5 | 42.6 | 17.0 | 26.3 | 16.9 | 31.3
| Oracle | 47.7 | 72.1 | 38.4 | 50.0 | 25.5 | 32.3 | 42.8 | 44.1

Table 2: Results of adaptation from small-scale to large-scale dataset (C2B).

Methods | mAP | Methods | mAP
---|---|---|---
Source only | 36.3 | MeGA-CDA [38] | 43.0
DA-Faster [7] | 38.5 | NL [19] | 43.0
SW-Faster [32] | 37.9 | SAPNet [21] | 43.4
MAF [13] | 41.0 | SGA-S [49] | 43.5
AT-Faster [14] | 42.1 | CST-DA [50] | 43.6
SOAP [43] | 42.7 |  $\text{A}^{2}$SFOD [8] | 44.9
SFOD [25] | 43.6 | IRG [39] | 45.7
LODS [24] | 43.9 | Ours | 47.0
SED(Mosaic) [25] | 44.6 | Oracle | 68.9

Table 3: Results of adaptation across cameras (K2C).

Methods | mAP | Methods | mAP
---|---|---|---
Source only | 40.5 | NL [19] | 43.0
MAF [13] | 41.1 | UMT [10] | 43.1
AT-Faster [14] | 42.1 | MeGA-CDA [38] | 44.8
HTCN [2] | 42.5 | CR-DA-DET [44] | 46.1
SED [25] | 42.3 |  $\text{A}^{2}$SFOD [8] | 44.0
SED(Mosaic) [25] | 43.1 | Ours | 57.8
IRG [39] | 43.2 | Oracle | 68.9

Table 4: Results of adaptation from synthetic to real scenes (S2C).

###  5.1 Experimental Setup

##### Task Settings.

Following the existing works [25, 8], we validate our method on the four
popular SFOD tasks which represent different types of domain shift, including
1) Cityscapes-to-Foggy-Cityscapes (C2F): Adaptation from normal to foggy
weather. 2) Cityscapes-to-BDD100k (C2B): Adaptation from small to large-scale
dataset. 3) KITTI-to-Cityscapes-Car (K2C): Adaptation across different
cameras. 4) Sim10k-to-Cityscapes-Car (S2C): Adaptation from synthetic to real
images. The A-to-B represents the adaption of the model pre-trained on the
source domain A to the target domain B.

##### Datasets.

There are five datasets used in the aforementioned tasks: 1) _Cityscapes_ [9]
is a street view dataset containing 5,000 images with instance-level pixel
annotation from different cities in different seasons, where 2,925 training
images and 500 validation images are used in the following experiments. 2)
_Foggy Cityscapes_ [33] is also a street view dataset similar to Cityscapes,
but its images are processed by three levels (0.005, 0.01, 0.02) of artificial
simulation of extreme foggy scenes. 3) _KITTI_ [12] is a widely used benchmark
dataset for autonomous driving which contains many images from different real-
world street scenes. There are only 7,481 training images used in the
experiments. 4) _SIM10k_ [18] is a synthetic dataset consisting of 10,000 city
scenery images of cars. 5) _BDD100k_ [47] is a large-scale open source video
dataset for autonomous driving, including 100k images from different times,
different weather conditions and driving scenarios.

###  5.2 Implementation Details

Our method is implemented based on PyTorch platform using detectron2 framework
[42]. Following the previous study [25], we use Faster-RCNN [31] with the
backbone of VGG16 pre-trained on the ImageNet as the base detection model in
our method. All images are scaled by resizing the shorter edge of the image to
600 pixels before training. The data augmentation strategy includes random
erasing, random horizontal flip, and color transformation. We adopt the SGD as
the optimizer with an initial learning rate of 8e-4, a decay rate of 0.1. The
batch size is set to 8.

The training process of our method consists of two stages: warm-up and
adaptation. In the warm-up stage, the learning rate increases gradually from 0
to 8e-4. The static teacher model freezes its weights and the dynamic teacher
model keeps updating during the first two epochs. In the fine-tuning stage,
the weights of the student model and the static teacher model are exchanged
per epoch, and the EMA rate of the dynamic teacher model is set to 0.999.
During evaluation process, we reserve the dynamic teacher model for inference
and choose the mean average precision (mAP) with an IOU threshold of 0.5 as
the evaluation measure.

Foggy level | Method | DT | ST | mAP
---|---|---|---|---
All levels | Source only | - | - | 30.7
Single-teacher | - | ✓ | 36.6
Single-teacher | ✓ | - | 38.0
Ours | ✓ | ✓ | 40.3
Single level | Source only | - | - | 18.2
Single-teacher | - | ✓ | 27.2
Single-teacher | ✓ | - | 32.9
Ours | ✓ | ✓ | 35.9

Table 5: Results of single-teacher and multi-teacher methods on C2F benchmark.
DT and ST represent the dynamic teacher and static teacher, respectively.

Weights flowing strategy | K2C | C2F | Avg
---|---|---|---
Baseline | 43.8 | 36.6 | 40.2
S $\longrightarrow$ ST | 46.8 | 39.6 | 43.2
DT $\longrightarrow$ S | 44.1 | 37.8 | 41.0
DT $\longrightarrow$ ST | 46.4 | 38.9 | 42.7
S $\longleftrightarrow$ ST (Ours) | 47.0 | 40.3 | 43.7

Table 6: Results of different exchange strategies on K2C and C2F benchmarks.
“Baseline” means training the proposed multi-teacher framework without any
weights flowing.

Figure 4: The training curves of each model within the multi-teacher framework
during the whole training process.

###  5.3 Comparison with Existing SOTA Methods

UDAOD and SFOD have a similar task setting. Therefore, we compare our method
with existing UDAOD and SFOD methods. Table 1-4 show the comparison results,
where “Source only” and “Oracle” represent the models which are only trained
in source domain or target domain data, respectively. They represent the upper
and lower performance bounds of the SFOD task.

##### C2F: Adaptation from Normal to Foggy Weather.

In real-world application scenarios, e.g., automated driving, object detectors
tend to encounter various complex weather conditions. To study the domain
shift caused by weather conditions, we perform the adaptation from normal
weather to foggy weather. For fair comparison, our experiments are conducted
in two manners: 1) All levels: Using all target data with three foggy levels
for training. 2) Single level: Using partial target data with a foggy level at
0.02 for training. The results are shown in Table 1. Our method achieves an
mAP score of 40.3%, which outperforms both the UDAOD and SFOD methods on this
benchmark.

##### C2B: Adaptation from Small-scale to Large-scale Dataset.

Annotating a large number of data for detection task can be very expensive and
time-consuming. Therefore, the most economical way is to transfer knowledge
from small-scale labeled datasets to large-scale unlabeled datasets. However,
different datasets exhibit varying degrees of domain shifts. To validate the
effectiveness of our method on such task, we transfer the source-pretrained
model from Cityscapes (source domain) to BDD100k (target domain). Following
the setting of previous studies [25, 8], we keep 8 categories in BDD100k that
are the same as Cityscapes. Since the detection performance of the category
“train” is always close to 0, we only report the mAP score of 7 categories in
Table 2. The results show that our method achieves very competitive
performance with the latest state-of-the-art SFOD method on this benchmark.

##### K2C: Adaptation across Various Cameras.

Due to different camera settings (e.g., angle, resolution, quality, and type),
domain shifts always occur in cross-camera images. To explore our method on
cross-camera images, we adapt the model trained on KITTI to SIM10k, a dataset
with images taken from real-world but different photographic equipment.
Following previous studies, we only evaluate the performance on “Car”
category. The results are reported in Table 3, where we can see that our
method obtains state-of-the-art performance on this benchmark.

##### S2C: Adaptation from Synthetic to Real Scenarios.

Synthetic images provide an alternative to address the challenges of data
collection and manual labeling. However, there is a substantial domain gap
between synthetic data and real data. To study the adaptation from synthetic
to real scenes, we use the model pre-trained on the entire Sim10k dataset as
the source model. The training set of Cityscapes is used as target data by
reserving car images and discarding other categories. Results in Table 4 show
that our method outperforms the existing SFOD approach by a large margin of
+13.8%, which demonstrates the superiority of our method on this benchmark.

###  5.4 Ablation study

##### Single-teacher VS. Multi-teacher.

We investigate the necessity of multi-teacher framework by comparing it with
single-teacher method on C2F benchmark. The single-teacher methods employ
either a static teacher or a dynamic teacher to guide the student learning
process, which no longer involves using the exchange strategy and consensus
mechanism. As shown in Table 5, our multi-teacher framework achieves the best
performance compared to the single-teacher frameworks on both foggy levels.
The success can be attributed to the superiority of exchange strategy and
consensus mechanism in multi-teacher framework.

##### Weights Flowing Strategy.

To verify the effectiveness of the proposed method, we also explore the
performance of other weights flowing strategies. The comparison results are
shown in Table 6, where $A\rightarrow B$ represents the single-direction
weights flowing strategy that model B copies the weights of model A, while
model A retains its weights, and $A\leftrightarrow B$ denotes our double-
direction weights flowing strategy. We can see that all weights flowing
strategies show the superiority to the baseline model that does not involve
any weights swapping. Moreover, the proposed double-direction weights flowing
strategy outperforms other single-direction strategies on both K2C and C2F
benchmarks. This again demonstrates the superiority of our method.

Source Only

ST (4999)

DT (4999)

Student (4999)

Foggy level = 0.01

Student (final)

Foggy level = 0.01

Foggy level = 0.005

Foggy level = 0.02

Figure 5: Detection results of different foggy-level images predicted by the
dynamic teacher, static teacher, student models trained in different times.
“DT (4999)”, “ST (4999)”, and “Student (4999)” represent the dynamic teacher,
static teacher, and student model in the $4999$-th iteration, respectively.
“Student (final)” represents the student model saved at the end of training.

###  5.5 Result Analysis

##### Training Stability.

The training curves of each model within our multi-teacher framework on the
four benchmarks are shown in Figure 4. Compared with the training curves of
the conventional MT framework (see Figure 2), the performance of the student,
static teacher and dynamic teacher models is stably improved and gradually
converges to a consistent point as the training progresses. We can see that
the training instability problem of conventional MT framework is effectively
alleviated by our method.

##### Visualization.

We conduct an analysis by visualizing the detection results of the static
teacher, dynamic teacher, and student models. This visualization is performed
by inputting several images with varying foggy degrees from the Foggy
Cityscape dataset [33]. The detection results of the three models for these
images are shown in Figure 5. It is evident that the two teacher models yield
varying detection results for each image, implying the potential
complementarity of their predictive results. This observation prompts us to
make a consensus on the divergent predictions of the two teacher models to
enhance the quality of pseudo labels. The effectiveness of the consensus
mechanism is further proven by the detection results of the student model
obtained at the final iteration, which has shown superior recall and accuracy
compared to the student model at the intermediate ($4,999$-th) iteration.

##  6 Conclusion

In this paper, we present a simple yet novel _Periodically Exchange Teacher-
Student_ method to tackle the training instability problem ignored by current
MT-based SFOD methods. Our method employs a static teacher model, a dynamic
teacher model, and a student model. At the end of each training period, we
exchange the weights between the static teacher and student models. Within
each period, the static teacher maintains its weights, while the student model
is trained using pseudo labels generated by both teachers. Meanwhile, the
dynamic teacher is continually updated using the EMA of the student model per
iteration throughout the whole training phase. The extensive experimental
results demonstrate the effectiveness of our method. Our method provides a new
insight for MT-based self-training methods.

## Acknowledgements

This work was supported by the Fujian Provincial Natural Science Foundation
(No. 2022J05135), the University-Industry Project of Fujian Provincial
Department of Science and Technology (No. 2020H6005), and the National Natural
Science Foundation of China (No. U21A20471).

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