  Deep networks have shown remarkable results in the task of object detection.
However, their performance suffers critical drops when they are subsequently
trained on novel classes without any sample from the base classes originally
used to train the model. This phenomenon is known as catastrophic forgetting.
Recently, several incremental learning methods are proposed to mitigate
catastrophic forgetting for object detection. Despite the effectiveness, these
methods require co-occurrence of the unlabeled base classes in the training
data of the novel classes. This requirement is impractical in many real-world
settings since the base classes do not necessarily co-occur with the novel
classes. In view of this limitation, we consider a more practical setting of
complete absence of co-occurrence of the base and novel classes for the object
detection task. We propose the use of unlabeled in-the-wild data to bridge the
non co-occurrence caused by the missing base classes during the training of
additional novel classes. To this end, we introduce a blind sampling strategy
based on the responses of the base-class model and pre-trained novel-class
model to select a smaller relevant dataset from the large in-the-wild dataset
for incremental learning. We then design a dual-teacher distillation framework
to transfer the knowledge distilled from the base- and novel-class teacher
models to the student model using the sampled in-the-wild data. Experimental
results on the PASCAL VOC and MS COCO datasets show that our proposed method
significantly outperforms other state-of-the-art class-incremental object
detection methods when there is no co-occurrence between the base and novel
classes during training.
