# Contrast R-CNN for Continual Learning in Object Detection

Kai Zheng
National University of Singapore
<EMAIL_ADDRESS>Cen Chen
Institute for Infocomm Research, Singapore
<EMAIL_ADDRESS>
###### Abstract

The continual learning problem has been widely studied in image
classification, while rare work has been explored in object detection. Some
recent works apply knowledge distillation to constrain the model to retain old
knowledge, but this rigid constraint is detrimental for learning new
knowledge. In our paper, we propose a new scheme for continual learning of
object detection, namely Contrast R-CNN, an approach strikes a balance between
retaining the old knowledge and learning the new knowledge. Furthermore, we
design a Proposal Contrast to eliminate the ambiguity between old and new
instance to make the continual learning more robust. Extensive evaluation on
the PASCAL VOC dataset demonstrates the effectiveness of our approach.

##  1 Introduction

Object Detection is a general localization task to predict the bounding box
and the category of the object in a scene. Recently, the development of
CNN[12, 29, 26] gives rises to great advances in object detection[2, 34, 23,
17, 28, 27]. However, the detector’s learning way is not the same with normal
training. One of the main gap is learning new objects in the dynamic world
continually. Humans are born to learn continued knowledge while grasping the
ability to keep the old knowledge. However, the state-of-the-art object
detectors lacks the ability to imitate this continual learning paradigm.
Although detectors can be fine-tuned for new tasks, they inevitably degrade
the old knowledge while learn new tasks – a problem called catastrophic
forgetting[9, 19].

In order to train an continual detector that can overcome the forgetting
phenomenon, PASCAL VOC[8] and COCO[18] is used to build a benchmark for this
new task. Due to the regulation of continual learning, the annotation of
previous learned old object has to be removed and only the new object’s
annotation is kept in the dataset. Although the model pretrained on old object
can be used for initialization on new task in the continual train step, the
result is still unsatisfactory. The feature representation learned in old task
can be drowned into the new task space, that is to say, the parameters learned
before can be changed drastically on new task. To tackle this problem,
continual learning of object detection is proposed to delve into the issue of
catastrophic forgetting in detection task.

Figure 1: The result of one distillation based continual detector. We
calculate the $\mathrm{mAP}$ over all categories, old categories and new
categories respectively to show the unexpected phenomenon of the distillation
based continual detector. In the figure, we observe an huge gap between the
$\mathrm{mAP}_{old}$ and $\mathrm{mAP}_{new}$. We denotes this phenomenon as
imbalanced performance in distillation based method.

Existing continual learning object detectors[24, 10, 21] use distillation to
mitigate forgetting. These methods requires the model trained on old dataset
to generate the constraints to regularize the weight updates. The constraints
can be obtained from either feature-level or response-level, which denotes the
feature map regularization and classification distribution regularization
respectively. However, these distillation-based method has some critical
deficiency, that is imbalanced performance and confusion classification. In
Fig.1, we show the Faster ILOD’s (one of the typical distillation based
method) performance on three datset splitting with regard to three
$\mathrm{mAP}_{all},\mathrm{mAP}_{old},\mathrm{mAP}_{new}$ metrics. We can
observe that there exists a huge gap between the performance on old and new
categories set. This implies the distillation method’s good performance on old
categories is at the expense of performance on new categories. We also delve
into the distillation based method and find that if we re-balance the loss
weight between the original loss and distillation loss, the huge gap on old
and new categories can be alleviate. This findings verifies our hypothesis
that the distillation put too heavy constraints on the detector to retain the
old knowledge, which prevents from learning the new knowledge. Another
confusion classification problem is the misclassification when performing
classifier on objects. From Fig.2 (left), we can observe that the
misclassification exists between old and new categories, and similar
categories. To some extent, we can attribute this misclassification problem to
the distillation paradigm. The distillation based method place too strict
constraints on old knowledge so that the detector minimizes the loss by
imitating all the old knowledge rather than learning the new knowledge. We
believe that without further constraints on discrimination between old and new
classes, the model can be drowned into the classification easily.

Figure 2: Confusion Matrix for all categories for misclassification
illustration. We select 15+5 setting for illustration, _e.g_. first fifteen
categories as old classes, the rest five categories as new classes. The
horizontal axis and vertical axis stand for the prediction category and
ground-truth category respectively. We annotate a green box for better
visualization of the old classes that misclassified to the new classes. The
left figure stands for the Faster ILOD (distillation-based method) while the
right figure represents our method, _i.e_. Contrast R-CNN. We observe that our
Contrast R-CNN can alleviate the misclassification effect between old and new
category. Note, the diagonal cell’s value is ignored when visualization for
better analysis.

A common approach to solve the confusion problem is to use a margin classifier
[7] or apply contrastive learning [6]. The exploration of self-supervise
learning has also arouse researchers’ interest in contrastive learning. In
self-supervised contrastive learning for image classification[5, 4], image
augmentation is utilized to build the positive pairs of two views from one
image while the negative pairs are those from different images.[13] extend the
contrastive learning into the supervise paradigm, where the images from the
same class are used to enrich the positive pairs. We think the contrastive
learning can help alleviate the confusion problem among classes. In detection
task, the label assignment can generate the semantic category for each
proposal, which can be used to build positive pairs in proposal-level.
Therefore, in our work, we explore the supervised contrastive learning in
continual detection. We believe the representation learned contrastively will
enlarge the decision boundary among different classes and ease the
misclassification.

We present Contrast R-CNN, a better approach for continual learning of object
detection. To build the contrastive objective, we need to obtain adequate
proposals from different categories, including old ones. However, the old
instances is missing in dataset due to the continual setting. Inspired by some
semi-supervised detection methods[1, 14], we propose an entropy-based data
distillation to obtain the “old instance”. With the proposals labeled by
ground-truth and distilled data, we can leverage the supervised contrastive
learning to do contrast based on categories.

To the best of our knowledge, we. are the first to bring contrastive learning
into continual learning of object detection. We benchmark Contrast R-CNN with
continual setting using the PASCAL VOC datasets under different experiment
setting, namely 10+10, 15+5 and 19+1. Our detector sets the new state-of-the-
art in two benchmarks, with up to +4.36% and +0.61% on all category and almost
+10% on new category in 10+10 and 15+5 settings.

Figure 3: Overview of Contrast R-CNN. Our framework mainly consists of two
parts, Data Distillation and Temporal Contrast R-CNN. The Data Distillation is
used to distill the label knowledge for continual training. To learn the
discriminative representation, we introduce a novel contrastive sub-framework.

##  2 Related Work

Continual Learning. Continual Learning refers to learn a new knowledge
continuously without losing the ability to retain the knowledge learned
previously.[16] uses the expanded classifier to extract the new knowledge and
applies cross-entropy and knowledge distillation loss on new class and old
class respectively.[22] stores some old data as exemplar and combine these old
data with new data to train the new model.[3] retains all the classifier for
distill knowledge on new task.[30] proposes to form a balanced old data and
new data for each batch to train the offset for alleviate the bias on new
class. In contrast to the continual learning of classification, rare work has
been explored in continual learning of object detection.[24] proposes to apply
distillation on proposals generated by external proposal generator.[10, 21]
both leverage the knowledge distillation on either the form of feature,
response knowledge or altogether.[15] also applies the distillation, but uses
other one-stage object detector, _e.g_. RetinaNet. All these continual
learning methods are based on distillation, which is the simple but effective
way to prevent from forgetting. However, in distillation based continual
learning methods, the new trained detector relies heavily on the previous old
trained model, in other words, the new model just imitate the response on old
class, not considering the relation between old and new class. In our
experiments, we find that the distillation based method puts too strict
constraints on old class and results in the bad performance on new class. In
contrast to the previous methods, we target designing an continual detector
performing balanced on both old and new class.

Contrastive Learning. The recent success of self-supervised methods has
validated the effectiveness of contrastive learning.[20, 31, 11, 5, 4]
formulate the way of contrastive learning as contrasting between positive and
negative pairs. The objective is to maximize the agreement between semantic
similar instances and differentiate the dissimilar.[13] extends the
contrastive learning into supervise learning, and the result is appealing. To
our knowledge, our work is the first to investigate contrastive learning in
continual learning of object detection.

Semi-Supervised Learning. Semi-supervised learning explores the way of
exploiting unlabeled data to facilitate the model training of on limited
annotated data. Pseudo labeling[1, 14], one of the simple and natural way to
extract the extra information from the unlabeled data, is one of the popular
class: the model generates pseudo labels on unlabeled data and thereby trained
a new model with this augmented data with labeled data.[32] proposes an
iterative teacher-student strategy that redoes the labeling assignments using
the teacher model and train the student model.[25] pretrains a detector using
the labeled data and uses it to generate pseudo labels on unlabeled data to
fine-tune the pretrained model with full data.[33] proposes an online pseudo
labeling strategy to keep the model that generates pseudo labels updating with
train. In continual learning of object detection, we aim to explore the
potential of leveraging the previous trained model in continual learning to
benefit the new model.

##  3 Problem Definition and Setups

Continual learning aims at learning a model in $t=1...T$ steps. In each
continual step, we present a dataset $D_{t}$ that consists of a set of pairs
$\\{(x_{n},y_{n})\\}_{n=1}^{N}$, where $x_{n}$ denotes the input image and
$y_{n}$ denotes the corresponding ground truth bounding box and category
annotation. The latter only contains the labels of current class $C^{t}$, and
all the other labels (_e.g_. old categories $C^{1:t-1}$ or future categories
$C^{t+1:T}$) is unaccessible. In the data setting, these bounding box
annotations are removed. However, the model at step $t$ is required to predict
all the classes seen before, _e.g_. $C^{1:t}$. In most continual learning[24,
21] setting, $T$ is set to 2, that is to say, $C^{1}$ and $C^{2}$ refer to old
categories $C_{old}$ and new categories $C_{new}$ respectively.

##  4 Method

Our proposed Contrast R-CNN involves a simple two-stage training. First, a
standard Faster R-CNN is trained on old data ($D_{train}=D_{old}$). Then, the
old detector is transferred to new data through fine-tuning on new dataset
with the old instance annotation removed ($D_{train}=D_{new}$).

In the following section, we show an overview of our Contrast R-CNN framework
(see Section 4.1), which consists of a Data Distillation module (see Section
4.2) and a Temporal Contrast R-CNN (see Section 4.3).

###  4.1 The overview of framework

As shown in Fig.3, our Contrast R-CNN framework is mainly composed of two
modules, namely, Data Distillation and Temporal Contrast R-CNN. In Data
Distillation, the well-trained Faster RCNN processes the input image and
distills the box based on the old knowledge learned in previous task. After
getting the raw distilled box, we apply the median entropy filter to process
the distilled box. Only the relative confident instances will be kept as final
distilled box. Then distilled box (old instance) and ground truth box (new
instance) will be fed into the next step as supervision. In Temporal Contrast
RCNN, it consists of two same Faster RCNN, namely Temporal and New, but with
different heads. For New Faster RCNN, a Contrast Head (as illustrated in Fig.
4) that mapping the roi feature into the embedding space is added parallelly
to the Cls Head and Box Head. For Temporal Faster RCNN, only Contrast Head is
kept. After getting the embeddings from Temporal and New Faster RCNN, namely
Key and Query, a supervised proposal contrast objective is employed to pull
the same category proposal together and push different category object apart
in embedding space. In parallel to the contrastive objective, the
classification and localization objectives are maintained under the distilled
plus ground-truth fashion.

###  4.2 Data Distillation

Data Selection. In semi-supervised object detection, previously trained model
is used to extract the knowledge in unlabeled data to provide extra
information. Likewise, in continual learning, we exploit the old model to mine
the useful knowledge in the new data. In our method, we propose a data
distillation module to distill the old knowledge in the form of box prediction
to benefit the new model training. We find that it is unreasonable to apply a
simple threshold for all the old classes, because different category has
distinctive distribution in the model prediction. Therefore, we introduce a
median entropy filter strategy. For each distilled bounding box $B_{i}$, the
strategy computes an entropy $e_{i}$ for the classification distribution
alongside its prediction category $c_{i}$. Each category $C_{j}$ will form an
entropy set $E_{C_{j}}=\\{e_{k}|c_{k}=C_{j},k=1,...,N\\}$, and compute the
median value in the set $E_{j}$ as the threshold $\alpha_{C_{j}}$ of this
category. After getting the threshold for each category, we apply the
category-specific median threshold filter on the raw distilled box, only the
box with the entropy lower than the median threshold will be kept.

Data Uncertainty. It is intuitive that the distilled box and ground-truth box
can not be equivalent due to the inherent noisy in prediction. Therefore, we
consider the entropy for each distilled box as the uncertainty weight in loss
calculation. For each box ($B_{i}$, $c_{i}$) with the entropy $e_{i}$, the
uncertainty weight $w_{i}$ can be computed as:

$w_{i}=\frac{\mathrm{max}\\{E_{c_{i}}\\}-e_{i}}{\mathrm{max}\\{E_{c_{i}}\\}-\mathrm{min}\\{E_{c_{i}}\\}}$

(1)

where,

$e_{i}=-\sum_{j=0}^{C}p_{i}^{j}\mathrm{log}p_{i}^{j}$

(2)

Figure 4: Illustration of Contrast Head. The contrast is composed of a
$\mathrm{FC-ReLU-FC}$ structure to map the roi feature into the embedding
space. Two $\mathrm{FC}$s are learnable in training.

$c_{i}$ is the $i$-th bounding box’s prediction category, $C$ is the the
summation of $C_{old}$ and $C_{new}$, $p_{i}$ is the classification
probability of for $i$-th bounding box.

In Faster RCNN, it sets a Cls Head and a Box Head to predict each RoI’s
classification score and box offset. For our Contrast R-CNN, we maintain these
two heads and compute the cross entropy loss and smooth L1 loss respectively.
The uncertainty weight is considered to re-scale the distilled box loss. The
weight is applied in both $L_{RPN}$ and $L_{RCNN}$ calculation. In our
experiments, we find that this uncertainty re-weighting strategy will make the
model robust to false prediction.

###  4.3 Temporal Contrast R-CNN

Proposal Contrast Embedding. Despite the introduction of data distillation as
the complement for ground-truth annotation, the continual detector is still
unable to learn a clear decision boundary because of the unconfident distilled
data. This leads to the confusion between old and new category or
misclassification between similar categories. To obtain more robust feature
representation from limited accurate annotation, our idea is to make the model
learn discriminative proposal embedding in continual learning stage.

To this end, we design a Temporal Contrast R-CNN with a Memory Bank for
enriching the discriminative feature. The Temporal Contrast R-CNN incorporates
two Faster RCNN, a normal Faster RCNN (New) and a Faster RCNN (Temporal). They
share the same feature extractor but with different heads. In order to map the
RoI feature into the embedding space, we introduce a Contrast Head parallel to
classification and box head. In the embedding space, the semantic distance can
be measured with angle.

In order for learning the discriminative feature, we set a contrastive
objective, which contrasts positive pairs against negative pairs[5, 11, 4]. To
provide more inter-class diversity, we establish a Memory Bank to store the
diverse negative pairs. The Memory Bank can be regarded as an FIFO queue,
where embeddings (Keys) of the current mini-batch from Faster RCNN (Temporal)
are enqueued, and the oldest are dequeued. The length of queue decouples from
the mini-batch size, allowing it to be large. Moreover, the embeddings from
the Faster RCNN (Temporal) are ensured to be feature consistency following the
special parameter update scheme[11]. The Faster RCNN (Temporal)’s parameters
are exponential momentum updated from the gradient updated Faster RCNN (New):

$\theta_{t}\leftarrow m\theta_{t}+(1-m)\theta_{n}$

(3)

Here, $m\in[0,1)$ is a momentum coefficient, which is used to control the
scale of evolving. $\theta_{t},\theta_{n}$ denote the parameters of Faster
RCNN (Temporal) and Faster RCNN (New) respectively. Only the parameters
$\theta_{n}$ are updated by back-propagation. In experiments, a relatively
large momentum (_e.g_. $m=0.999$, our default) ensures the $\theta_{t}$ evolve
smoothly and slowly.

| 10+10 setting | 15+5 setting | 19+1 setting
---|---|---|---
|  |  | $\mathrm{mAP}_{old}$ | $\mathrm{mAP}_{new}$ | $\mathrm{mAP}$ | $\mathrm{mAP}_{old}$ | $\mathrm{mAP}_{new}$ | $\mathrm{mAP}$ | $\mathrm{mAP}_{old}$ | $\mathrm{mAP}_{new}$ | $\mathrm{mAP}$
Normal R-CNN (fine-tune) |  |  | 13.88 | 62.50 | 38.19 | 18.17 | 56.14 | 27.66 | 5.74 | 78.94 | 9.40
Faster ILOD[21] |  |  | 68.23 | 53.10 | 60.67 | 69.04 | 57.64 | 66.19 | 68.93 | 70.50 | 69.01
Contrast R-CNN (ours) |  |  | 66.62 | 63.44 | 65.03 | 66.82 | 67.23 | 66.92 | 68.02 | 67.82 | 68.01

Table 1: Results on three different benchmark settings. We use
$\mathrm{mAP}_{old}$, $\mathrm{mAP}_{new}$, $\mathrm{mAP}$ as metrics to
evaluate the performance on old, new and overall categories. Normal R-CNN
(fine-tune) refers to fine-tuning on the new data simply. The Faster ILOD’s
result is reproduced by us based on the publicly released code.

| 10+10 setting | 15+5 setting | 19+1 setting
---|---|---|---
|  |  | $\mathrm{mAP}_{old}$ | $\mathrm{mAP}_{new}$ | $\mathrm{mAP}$ | $\mathrm{mAP}_{old}$ | $\mathrm{mAP}_{new}$ | $\mathrm{mAP}$ | $\mathrm{mAP}_{old}$ | $\mathrm{mAP}_{new}$ | $\mathrm{mAP}$
Contrast R-CNN w/o memory bank |  |  | 63.31 | 64.34 | 63.83 | 65.35 | 67.97 | 66.00 | 64.26 | 80.06 | 65.05
Contrast R-CNN w/o median selection |  |  | 65.61 | 63.73 | 64.67 | 66.64 | 67.22 | 66.78 | 67.85 | 66.45 | 67.78
Contrast R-CNN w/ memory bank, median selection |  |  | 66.62 | 63.44 | 65.03 | 66.82 | 67.23 | 66.92 | 68.02 | 67.82 | 68.01

Table 2: Ablation Study. Three rows refer to the method proposed by us that
without memory bank, without median selection, with memory bank and median
selection respectively.

Proposal Contrastive Loss. For the contrastive objective, we construct the
embeddings from Faster RCNN (New) and embeddings with the same category label
in Memory Bank as positive pairs, while others as negative pairs. Then we
formulate our Proposal Contrastive Loss $L_{PC}^{i}$ as follows:

$L_{PC}^{i}=-\frac{1}{N_{c_{i}}}\sum_{j=1,j\neq
i}^{N}\mathbbm{1}\\{c_{i}=c_{j}\\}\cdot log\frac{\mathrm{exp}(z_{i}\cdot
z_{j}/\tau)}{\sum_{k=1}^{N}\mathbbm{1}_{k\neq i}\cdot\mathrm{exp}(z_{i}\cdot
z_{k})/\tau}$

(4)

$N_{c_{i}}$ is the number of embeddings with the same label $c_{i}$ in Memory
Bank, $N$ is the total number of embeddings in Memory Bank, and $\tau$ is the
hyper-parameter temperature as in InfoNCE[20].

There exists the uncertainty variance among the proposals, and the reason
mainly lies in two-fold: (i) the uncertainty in roi-distilled box’s matching.
The lower the IoU is, the more the uncertainty is. (ii) the uncertainty in
distilled box itself. The latter one comes from the pretrained model’s noise.
To ameliorate the effect of uncertainty, we introduce two variables
$\phi_{1},\phi_{2}$ to model each uncertainty respectively:

$\phi_{1}^{i,j}=IoU(P_{i},R_{j})$

(5)

$\phi_{2}^{i}=w_{i}$

(6)

where $P_{i},R_{j}$ represent the $i$-th distilled box and $j$-th roi. The
calculation for $w_{i}$ refers to Eq.1. Then, the $L_{PC}^{i}$ is reformulated
as

$L_{PC}^{i}=-\frac{1}{N_{c_{i}}}\cdot\phi_{2}^{i}\cdot\sum_{j=1,j\neq
i}^{N}\mathbbm{1}\\{c_{i}=c_{j}\\}\cdot\phi_{1}^{i,j}log\frac{\mathrm{exp}(z_{i}\cdot
z_{j}/\tau)}{\sum_{k=1}^{N}\mathbbm{1}_{k\neq i}\cdot\mathrm{exp}(z_{i}\cdot
z_{k})/\tau}$

(7)

##  5 Experiments

Implement Details. The results for Faster ILOD[21] are generated using their
public implementation. For fair comparison, we reproduce the result under the
same experiment setting. We use the same backbone network (ResNet-50) and
similar training strategy stated in their paper. In the first step of training
an old model, we set the learning rate to 0.001, decaying to 0.0001 at 30k
iterations, weight decay is set to 0.0001 and the momentum is 0.9. The total
training iteration is 40k for PASCAL VOC. In the following continual training
step, learning rate is set to 0.001, other hyper-parameters keeps the same as
the first step.

Dataset and Evaluation. We evaluate our method on PASCAL VOC 2007. VOC 2007 is
composed of 5K images in the trainval set and 5K images in the test set of 20
object categoires. For the evaluation metric, we use the standard mean average
precision (mAP) at 0.5 Intersection over Union (IoU). Evaluation of the VOC
2007 is done on test set. Table 1 shows the results for continual setting,
where 10+10, 15+5, 19+1 represents the old+new category split (_e.g_. 10+10
refers to first ten categories for old first step training, the rest ten
categories for continual step training). In order to delve into the
performance on old and new category, we calculate the mAP on old and new
categories respectively, denoted by $\mathrm{mAP}_{old}$,
$\mathrm{mAP}_{new}$. We observe that in 10+10 and 15+5 setting, our Contrast
R-CNN has achieved SOTA in both $\mathrm{mAP}$ and $\mathrm{mAP}_{new}$.
Especially in $\mathrm{mAP}_{new}$, we outperforms Faster ILOD a large margin
by almost +10%. This can be attributed to the fact that the distillation-based
method’s bad performance on new class. From the Table 1, it is obvious that
the huge gap exists between $\mathrm{mAP}_{old}$ and $\mathrm{mAP}_{new}$ on
all three settings. This fact convinces us that the distillation pose too
strict constraints on continual model to retain the old knowledge, which
inevitably hold back the learning of new knowledge. This spectulation exactly
help interpret the more old categoires are, the better its performance is,
that’s why Faster-ILOD performs slightly better than us in 19+1 setting.
Meanwhile, this severe imbalanced performance between the old and new
knowledge reveals the critical deficiency in distillation-based method in
continual learning, which is ignored by most researchers[21, 10, 15]. In
contrast, our method shows an reasonable balanced performance on both
knowledge.

##  6 Ablation

To evaluate the contributions of each component, we conduct a series of
experiments and report the ablation analysis in Table 2 on PASCAL VOC dataset
in 10+10, 15+5, 19+1 settings. As already noticed, Contrast R-CNN without the
memory bank leads to a great degradation, where
$\mathrm{mAP}_{old}/\mathrm{mAP}$ suffers from -3.31%/-1.2%, -1.47%/-0.92%,
-3.76%/-2.96% in three settings. The median selection strategy can help
improve the accuracy of old categories slightly.

##  7 Conclusion

In this paper, we paved a new way for future research on Continual Learning
for Object Detection, which is an emerging and practical domain in computer
vision. In particular, our Contrast R-CNN demonstrates the new perspective in
dealing with the missing annotation and saving the object from being
misclassified. Our method achieves state-of-the-art results in most settings.
For a broader impact, our work proves the plausibility of incorporating the
contrastive learning into the continual object detection. We hope our work can
give more inspiration for researchers.

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