Few-Shot Batch Incremental Road Object Detection via Detector Fusion
Anuj Tambwekar *PES University
anujstam@gmail.com

Kshitij Agrawal

Anay Majee
Anbumani SubramanianIntel Corporation
firstname.lastname@intel.com

Abstract
Incremental few-shot learning has emerged as a new andchallenging area in deep learning, whose objective is totrain deep learning models using very few samples of newclass data, and none of the old class data. In this work
we tackle the problem of batch incremental few-shot roadobject detection using data from the India Driving Dataset(IDD). Our approach, DualFusion, combines object detectors in a manner that allows us to learn to detect rare objects with very limited data, all without severely degrading
the performance of the detector on the abundant classes. In
the IDD OpenSet incremental few-shot detection task, weachieve a mAP50 score of 40.0 on the base classes and an
overall mAP50 score of 38.8, both of which are the highest
to date. In the COCO batch incremental few-shot detection task, we achieve a novel AP score of 9.9, surpassing
the state-of-the-art novel class performance on the same byover 6.6 times.

1. Introduction
Object detection is a challenging task that aims to detect
the presence and location of desired objects in an image.
It is a crucial part of smart transportation, where it is usedby both autonomous vehicles, as well as advanced driverassistant systems (ADAS) [25].
Current state of the art object detection networks such as
Faster R-CNN [19], YOLO [18] and RetinaNet [12] perform well on canonical object detection datasets such as
PASCAL VOC [3] and COCO [13]. These object detection networks are reliant on large-scale annotated datasets
for training. Acquiring such large volumes of data is both
expensive or requires tremendous amount of human effort.
The traditional object detection datasets, are well-balanced
and contain a sufficiently large number of examples per category. However, in a real world, classes of interest may berare and hard to come by, thus resulting in a data scarcity
due to the nature of the objects in question. This problem is* Work done as an intern at Intel Corporation

one of the key challenges in detecting rare objects found on
Indian roads [15], where there is a significant class imbalance between the frequently occurring classes, and the rarer
classes.
Few shot learning has been proposed to overcome thesechallenges and learn with limited amount of data. Whilesignificant progress has been made in this area in both theclassification [26, 22, 23] and detection [10, 9, 27, 4, 30]
tasks, most techniques assume that the data from the abundant classes is always available. This requirement cannot
always be met, as there may be scenarios where we require
to increase the capability of the model to detect new classes
without access to old data. For example if the models are
deployed in autonomous vehicles and the vehicle needs todetect new objects on the road. This challenge of learning todetect new classes in addition to existing classes, while only
being exposed to the new classes, is termed incremental object detection. As described in [17], learning one class at
a time is termed class incremental object detection, whilelearning to detect multiple new classes in one attempt istermed batch incremental object detection. Few-shot object detection techniques also suffer from class confusion[29], a phenomenon where objects are located correctly, butclassified into the wrong class. Base-novel confusion refers
to this incorrect classification, where an object belonging toa base class is incorrectly labeled as an object of a novel
class, or vice-versa.
Most existing works treat few-shot detection and incremental detection as two separate tasks, when in reality it isimportant to address both simultaneously. One of the key
requirements of existing incremental learning techniques[20, 2, 7] is a reliance on having a large amount of data
corresponding to the new classes, making them ineffective
in a few-shot setting. Additionally, incremental learningtechniques are prone to catastrophic forgetting [5, 16], aphenomenon where the incremental detector fails to detect
the previously learnt classes and thus forgets the previously
learnt features. In a safety-critical environment such as an
ADAS or an autonomous vehicle, it is crucial to ensure thatnone of the the prior learning is forgotten, making existing
techniques unsuitable for these tasks.

In this work, we propose DualFusion, a method to address few-shot batch incremental learning to detect road objects using only 10 annotated instances of each novel class,and without any base class data. The proposed architecture combines class-specific object detectors using a proposal segregation technique. This improves the classification ability of the combined detector to detect novel andbase classes from overlapping regions of interest, while stillretaining knowledge from the individual classifiers as no retraining is involved. We specifically design this techniquefor contextual datasets like road object detection where rarenovel classes are found alongside bases classes.
The rest of this paper is divided as follows - Section 2
provides an overview of the background and related work.
Section 3 describes the architecture of DualFusion in detail, along with its training strategy. Section 4 goes over theexperimental setup used to validate DualFusion on road object detection with the India Driving Dataset (IDD)[24] aswell as more generalized cross-domain setting on COCO,
where it achieves SOTA performance on novel classes. Finally in Section 5 we conclude and discuss the advantages,
drawbacks and scope for extension.

2. Background and Related Work2.1. Object Detection
Standard object detection techniques require large
amount of data to train and aim to localize and classify objects present in an image. They are broadly categorized into
two groups - single stage detectors and two-stage detectors.
Single Stage detectors such as SSD [14], Retinanet [12] andYOLO [18] extract regions of interest and classify objects
in a single step. Meanwhile, two stage detectors such as
Faster R-CNN [19] first identify regions of interest via a
Region Proposal Network (RPN), and then determine thebounds of the object and its class based on the contents of
the region of interest. These techniques cannot be applied
directly when dealing with heavy class imbalance and when
only a limited amount of data is available as shown by priorwork such as [9, 27].

2.2. Few-Shot Object Detection
Few-shot learning [28] is a technique that learns a data
distribution using very limited training data. Formally, if N
new classes are learnt in the few-shot setting, and there are
K training examples per class, it is termed N -way K-shotlearning.
Recent successful few-shot object detection techniques
are episodic approaches that operate by reweighting the features between classes. such as Feature-Reweight [9]. MetaRCNN [30] extends the previous approach to a Faster RCNN based network. It is a meta-learning technique thatuses an episodic training regime to attempt to train the net-

work to learn how to learn better. In a similar fashion Kim etal. [10] uses a meta-learner that employs knowledge transfer to create class-vector prototypes that are used to provide
weights to the features. MetaDet [29], uses meta-learning tocreate a model that learns to transform weights learnt during
the few-shot training stage to large-scale training weights.
Two-stage finetuning [27], provided a different viewpoint, and instead suggested that the lower layers of the detectors produced class agnostic features, while the higher
layers produced class specific features. The technique,known as FsDet simply called for fine-tuning the final layerusing cosine similarity in order to learn the new classes.
However, FsDet is reliant on a well balanced training
dataset which contains both the base and the novel classes
in the fine-tuning stage. This reliance on base class labels
during the fine tuning stage makes it unsuitable for an incremental learning setting.
One of the best performing few-shot object detection
techniques is FewX [4], which uses attention maps as away to search for the presence of the novel classes in animage. In theory, this allows the network to detect novel
classes without any kind of fine-tuning, if trained on a sufficiently varied dataset, but for context specific situations,fine-tuning greatly improves performance. While this architecture claims the highest performance on the COCO[13] dataset, it suffers from the glaring limitation of only
being able to detect the novel classes. FewX uses attention as a mechanism to find candidate proposals and thenclassify objects in an image. Hence, if no base classes arepresent in the support set, FewX will never be able to detectthem. This makes it an impractical solution when any of theclasses have sufficient data and need not be treated as fewshot, as few-shot performance is always less than standard
training performance. It also poses a challenge when accessto the base class data is withheld.

2.3. Incremental Object Detection
Incremental object detection learns to detect new classes
in addition to the existing classes. In this setting, the detectors are trained to retain the base class performance andwhile only being exposed to the new classes. Incremental
learning can be divided into two categories based on thenumber of classes being added in every stage - single classincremental learning where new categories are introduced
one at a time, and batch incremental learning, where multiple new categories are learnt together.
Most existing incremental object detection techniques[21, 2, 7] operate on knowledge distillation [8]. [21] uses abalanced loss combing distillation as well as cross-entropy
to teach a new network to detect new classes, as well as
to attempt to transfer the knowledge from the old detector.
[2] uses intermediate proposals, along with two terms theycall hint loss and confidence loss, to transfer feature maps

Figure 1: Architecture of DualFusion. Components in orange denote parts of the base detector, those in blue denote parts of
the novel detector, and those in green denote the Fusion Network. Coloured arrows denote the flow of the various proposal
categories - orange for base categories, blue for novel, and green for overlapping.
from the original model. [7] expands the foreground domain of the region proposal network, and uses a nearestprototype classifier as well as distillation loss to expand thedetector’s ability to add in new classes. The chief drawback
with all these techniques is that they assume an abundanceof data corresponding to the new classes. The current stateof-the-art technique, Open-ended Centre Net (ONCE) [17],is the only existing few-shot incremental object detection
network, and works by re-weighting image features basedon a class code generator. The reported quantitative results
for the novel classes are, however, very low.

3. Our Approach : DualFusion
In this work we target few-shot batch incremental object
detection. In this setting, the objective is to learn to detect
multiple novel classes at once, while retaining the ability todetect the base classes, using only novel class data in theincremental addition stage.
One of the biggest challenges with any incremental
learning scenario is catastrophic forgetting [5]. While techniques such as knowledge distillation [8] have been proposed to combat this limitation, they still result in a heavy
loss of performance on the base classes [1, 21]. We propose
a simple yet powerful solution to circumvent this problem- instead of re-training the existing network, or training a
new network using a distillation loss, we retain the original
network, and combine it with another network specificallytrained to discriminate for novel classes. Our approach canthus be extended to detect multiple novel classes incrementally without the need for base class data.
Formally, we can define the task as follows - Given a setof base classes B, a detector capable of detecting the baseclasses DB , and novel classes N , our goal is to create a new
detector D′ , capable of detecting all classes from B and N ,while only having access to 10 annotated instances of eachclass in N , and no annotated instances of classes in B.

3.1. Architecture
The DualFusion architecture comprises of 3 components• A Faster R-CNN based base class detector to detect
the base classes, that is trained only on base class data,with no exposure to novel classes.
• A few shot novel class detector based on FewX modified, so as to combine the outputs of each attention
head into an intermediary layer before the classifierand box regressor.
• The Fusion Network. This network is designed to classify objects that are flagged as possible candidates byboth the base class and the novel class detector. It isakin to an ROI head predictor that learns to combine
the features obtained by the base and novel detector toclassify objects.
Figure 1 provides a pictorial representation of the architecture. The black arrows indicate the flow of data before andafter the proposal segregation step. The coloured arrows
indicate the flow of data corresponding to the various proposal categories. Orange arrows denote the flow of data corresponding to the base class proposals, Blue arrows denotethe flow of data corresponding to the flow of novel classproposals, and green arrows denote the flow of data corresponding to the overlapping proposals.

3.1.1

Architectural Setup and Flow

The final DualFusion model comprises of the three models mentioned earlier. Details about the training process
are described subsequently in Section 3.2. In short, thefaster RCNN base class detector is trained only on baseclasses, the novel class FewX detector is only trained on
few-shot samples of the novel classes, and the Fusion network is trained using features extracted from the mineddataset whose collection is described in the Training section.
An input image is passed through the backbone and RPNof both the novel class and base class detectors. The proposals are then segregated into 3 categories - base proposal
regions, novel proposal regions and overlapping proposal
regions. We describe why this is needed and how it is exactly done in Section 3.1.2. The base proposals are passed
through the base class detector as usual to obtain a set offeatures, which are then converted into a detection output.
In a similar fashion, the novel class detector acts on novel
proposals and identifies the rare novel classes.
The overlapping proposals are passed to both detectors’
feature extraction networks, and the penultimate outputs
and the predicted boxes and logits are extracted. Theselatent feature and prediction combinations are first passed
through a single fully-connected layer, following which
they are concatenated and passed to the fusion network,
which learns to distinguish between the base and novel detections, thereby reducing base-novel confusion. Each input is passed to a corresponding two layer deep fully connected network that uses the SeLU [11] activation function- this allows the features to be transformed into the samefeature space. The transformed features are then combined
and passed through two layers with ReLU activation, for theclass score and bounding boxes predictions.
As result, we have a collection of three detection outputs at the end of a forward pass of an image. These detection outputs are combined, so as to remove overlapping
predictions of the same region, and to reduce intra-novel
confusion, by comparing the IoU between the three detection sets and selecting the detections with larger confidence
scores whenever the IoU between detections from different
detectors exceeds 0.5. This step is similar to the standard
Non-Maximal Suppression (NMS) post-processing step ofobject detectors, however it considers the output of different
detectors in the same region, as opposed to detections of thesame class.
3.1.2

Proposal Segregation

The obvious question to ask is - “Why can’t we just use two
detectors?”. A simple strategy of using two different detectors does not work as novel objects with similar features
may be incorrectly classified as base class objects and vice-versa. Additionally, regions with features present in boththe base and novel classes may be detected by both detectors, and thus the same region may be marked as two different objects (see Figure 4 for an example of the same). We
term this phenomenon as base-novel confusion. Our designof fusing the intermediate outputs of each of the networks
allows for a degree of communication between detectors,
and thereby reduces this confusion. We utilize FewX as anovel class detector as it is, at the time of writing, the best
performing few-shot detection model on COCO. Additionally, it is a two-stage approach, which allows us to implement proposal segregation.
Proposal segregation allows us to retain base and novel
class performance by using the appropriate detector in regions without confusion. We first generate a collection ofcandidate proposals from the base detector PB and candidate proposals from the novel detector PN . Following this,we compute the element-wise Intersection over Area (IoA)
between PB and PN . The proposals from PB whose IoA
with every proposal from PN is below a certain threshold
are considered valid base proposals. Likewise all proposals
from PN whose IoA with every proposal from PB is below
the threshold are considered valid novel proposals. The restof the proposals are considered as overlapping proposals.
We use IoA instead of Intersection over Union (IoU), as itprovides a better estimate of proposal overlap between baseand novel classes, and used a threshold value of 0.5.

3.2. Training
DualFusion is trained over a series of 4 steps• Step 1. Base Model Training: To setup the basetrained models, we train a Faster R-CNN based detector to detect IDD base classes. Following this, we train
a FewX based model on the IDD base classes, using
just 10 images from each class - this is done in order
to allow for domain adaptation. The Faster R-CNN detector is frozen and from this point forward, we assume
we have no access to the base training data.
• Step 2. Novel Model Fine-Tuning : We fine-tune
the FewX based few-shot object detector specificallyto detect novel classes. The only data we have accessto at this point are driving scene images where novel
classes are present. We assume 10 images per novel
class, giving us a total of 10 × N training samples,
where N is the number of novel categories. Furthermore, we only use the ground truth annotations of thenovel classes present in these training samples.
• Step 3. Fusion Network Dataset Creation : Due tothe nature of images in IDD, the novel classes occur
in driving scenes which contain the presence of baseclasses. As such, while the training samples only contain ground truth annotations of novel classes, thereexist un-annotated instances of some of the base classobjects in the training data. By using the predictions
from base class detector, we generate pseudo-groundtruth labels for the base classes and use them for training. Any objects detected with a bounding box IoU of
over 50% with a novel class are removed. It is not required, nor expected of the data to contain instances ofevery base object class as part of this scenario, as thegoal is to simply teach the detector how to distinguishbetween the features of the base and novel detectors.
• Step 4. Fusion Network Training : Combining thepseudo labels of base class from Step 3, we generate
the final training set. We train the fusion network thatcombines features from both the base and novel detectors to provide predictions in regions of high basenovel confusion.

(a) Tractor(b) Street Cart(c) Water Tanker(d) Excavator

Figure 2: Examples of novel classes in IDD-OS

4. Experimental Setup and Results
To validate the efficacy of DualFusion as a batch incremental few-shot learning paradigm, we run a set of tests
comparing it to existing techniques for both the India Driving Dataset (IDD) and COCO.

4.1. IDD
For our experiments we use the IDD-OpenSet (IDD-OS)
split as described by [15] over conventional driving datasets
such as Berkeley Deepdrive [31] and KITTI [6], due tothe fact that it possesses the additional challenge of large
amount of class imbalance, along with a well-defined splits
for consideration of base classes and novel classes. Examples of the objects belonging to the novel categories can beseen in Figure 2.

Given an object detector D trained on the IDD Base Set
classes - Autorickshaw, Bicycle, Bus, Car, Person, Motorcycle, Rider, Traffic Light, Traffic Sign, and Truck, train adetector D′ , that can detect the rare novel classes from IDDOS - Tractor, Street Cart, Water Tanker and Excavator, ina batch incremental setting. The detector D′ is trained todetect novel classes with only 10 annotations of each, while
simultaneously having access to the original detector D.
We compare the performance of DualFusion to various
techniques. We test two Faster R-CNN finetuning strategies. The first is standard Faster R-CNN Finetuning, where
we simply add 4 additional outputs to the detector and train
it on the novel classes. The second is Faster R-CNN LastLayer Finetuning, where we finetune only the last layer. For
both, we train for 2000 iterations at a learning rate of 0.002
and a learning rate decay step of 1000 iterations. We also
test FsDet without the rebalancing finetuning stage as discussed by the authors in [27]. In this step, the merged network is finetuned on a balanced dataset containg annotations from all the classes. As this requires base class data,
we skip this step, and instead use the model obtained after combining the model trained on the base data, and themodel obtained by training on novel data. Lastly, we compare with FewX. All five networks are initially trained on
IDD-Base, following which, they are trained on only the 10
novel examples from IDD-OS. As per [15], we report themAP50 score for each category. Feature Reweight [9] was
not used due to out-of-memory issues while training. Our
Faster R-CNN base detector roofline gave us a base class
mAP50 detection score of 50.1. We found that fine-tuning
FewX was required in order to allow it to adapt to the driving domain. The FewX network and the DualFusion novelarm were trained for 2500 iterations, at a learning rate of
0.002 with steps at 1000 and 1500 iterations and a batch
size of 2.
Results : Table 1 shows a comparison between the
mAP50 scores of DualFusion and the prior mentioned Fewshot detection techniques. From the table we can observethat DualFusion is able to learn the novel classes extremely
well, while significantly retaining base class knowledge, resulting in the highest base mAP50 and the the highest overall mAP50 performance. Additionally, DualFusion also
performs better for certain novel classes (street cart andexcavator) compared to FewX, due to its ability to reducebase-novel confusion. Our technique is able to obtain 80%of the base class performance of a conventional Faster RCNN, achieving a mAP50 of 40.0, all while extending theability of the detector to detect the novel classes. A few example detections predicted by DualFusion on IDD-OS canbe seen in Figure 3 where they are contrasted with detections predicted by Faster R-CNN finetuning method. As
seen in the figure, while finetuning is able to learn to pickout novel classes, it depicts severe base class forgetting. Du-

Method
Faster R-CNN Finetuning
Faster R-CNN Finetuning (only last layer)
FsDet (no rebalancing finetuning)
FewX
DualFusion (ours)

Tractor
16.8
12.9
0.0
48.9
30.5

Novel Classes (mAP50 )Street Cart Water Tanker
8.5
0.1
9.1
0.1
0.0
0.0
50.1
31.5
28.6
32.0

Excavator
21.6
13.2
0.0
22.0
42.1

mAP50,novel

mAP50,basemAP50,all

11.8
8.8
0.0
38.1
33.3

0.0
0.0
9.42
40.0

3.4
2.5
6.73
10.9
38.8

Table 1: mAP50 Scores on the IDD Open Set 10-shot Batch Incremental Task. Cells filled with ’-’ indicate that the techniquecannot be used for the task, and hence a score is not applicable. While FewX performs well on novel classes, it cannot detect
the base classes. On the other hand, DualFusion achieves the highest base class and overall class performance on the task,
and is clearly able to retain base class knowledge while learning to detect the novel classes.

(a) Tractor(b) Street Cart(c) Water Tanker(d) Excavator

Figure 3: Examples of novel (green bounding boxes) and base (blue bounding boxes) class detections on IDD-OS via Faster
R-CNN Finetuning (top row) and DualFusion (bottom row). Base class forgetting and erroneous novel class detections areclearly seen in Finetuning, but both are avoided by DualFusion.
alFusion is able to retain base classes as well as provide
better detections for the novel classes. While FewX does
outperform DualFusion in two novel classes, it is unable todetect any base classes due to the absence of base class supports in the incremental setting, showcasing DualFusion’s
superiority.
Figure 4 shows how the FusionNet helps reduce confusion between the base and novel classes, by handling overlapping proposal regions between the base and novel detectors. As seen in the figure, the autorickshaw class is incorrectly detected, as part of the excavator. By using the fusion
approach, this region is evaluated as being part of the excavator, which gets rid of the incorrect prediction.

4.2. COCO
DualFusion is specifically designed for contextual
datasets like IDD, as it leverages heavily on the presence ofthe novel classes in settings with abundant bases classes. To
benchmark our DualFusion approach in a more generalized
setting, we compare our network’s performance to ONCE
[17] on the COCO incremental learning task.
We use the incremental batch of 20 classes setup as de-scribed by [17]. Our training methodology is identical to
the training described in Section 3, with the only change
being the use of the COCO dataset as opposed to IDD. We
treat the 20 classes in common with the PASCAL VOC [3]dataset as novel (aeroplane, bicycle, bird, boat, bottle, bus,car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train and tv/monitor), and theremainder as base classes. We consider the 10-shot setting
once again, and the training examples used are the same as[4]. Due to computational constraints, we only trained ourbase Faster R-CNN on a single GPU, using the default training settings. As such, the base class Faster R-CNN detector
was only able to reach a base class AP score of 19. We use
the default FewX training settings to train our novel classdetector. Our fusion model was trained for 10 epochs at alearning rate of 0.001. We used an IoA overlap thresholdof 0.8 during the fusion training stage. The regular FewX
model achieved an AP score of 13.1 on the novel classes,
but as stated earlier, it fails for the base cases. As such we
do not include it in Table 2.
Results: As seen in Table 2 the results clearly show that
DualFusion has superior novel class performance compared(a) Without FusionNet - Autorickshaw is incorrectly detected(b) With FusionNet - No incorrect Autorickshaw detection

Figure 4: Example of an erroneous prediction removed via fusion. The FusionNet is able to prevent an object from beingclassified as both a base class and a novel class object by handling proposals where such confusion may occur.
MethodFine-Tuning
Feature Reweight
ONCE
DualFusion (ours)

Base Classes AP
2.8
3.7
17.9
10.8

Novel Classes AP
0.6
1.5
1.2
9.9

All Classes AP
2.3
3.1
13.7
10.6

Table 2: Performance on COCO 20-Class Batch Incremental Few-shot Setting. DualFusion results in the highest
novel class performance compared to the other techniques,achieving 6.6 × the performance of the closest competitor.

to ONCE, while still retaining a good degree of base classperformance. The results for Finetuning, Feature Reweightand ONCE are from [17]. Finetuning refers to vanilla finetuning of a CentreNet [32] model on novel class data, andFeature Reweight is a CentreNet implementation of [9].
Since DualFusion is designed specifically for contextual
settings like driving, it is encouraging to see good performance, even in a generalized object detection challenge
like COCO, achieving almost 8 times the novel class performance. Granted, our initial base class performance wasonly 19 AP and as such, we retain over 56% of the baseclass performance in this setting, and retain roughly 76% of
FewX’s novel class performance. Interestingly, we also observed that 71 of the 80 classes were detected during the Fusion Network training stage, indicating our method of generating pseudo labels for base classes is quite efficient evenin generalized settings. Moreover, it enables us to train thedetector with no knowledge of base classes in a truly incremental setting. These results show that our work is applicable to more than just road object detection, and is applicable
to wider settings.

5. Conclusion
In summary, through this work we present an architecture for batch incremental object detection for road object

detection, that employs the simple, yet effective strategy offusing specialized object detectors. DualFusion allows forincrementally adding the ability to detect new classes, using less data, with the only requirement being access to datafrom all the novel classes. This approach allows for a highdegree of base class performance retention and preventionof catastrophic forgetting, while providing extremely highnovel class performance. We solidify our claims by obtaining the highest base mAP5 0 and overall mAP5 0 scores(40.0 and 38.8 respectively), on the IDD-OS incremental
task. While this technique is designed for road object detection, it is also suitable for other object detection problems
where the classes of interest do not occur in isolation. Thiscan be seen by its performance on the COCO dataset where
it achieves the highest recorded novel class detection scoreof 9.9 AP in the 20 class few-shot batch incremental setting.
The chief limitation of DualFusion is its reliance to permanent access to novel class data. While it requires access to the base class data only once, all the novel few-shotdata must be retained. Additionally, the evaluation time
grows substantially with the addition of more novel classes.
Techniques to overcoming these limitations could pave theway for lifelong few-shot object detection, which in turnwould greatly increase the robustness and reliability of autonomous vehicles and similar safety critical systems.

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