  Training of object detection models using less data is currently the focus of
existing N-shot learning models in computer vision. Such methods use
object-level labels and takes hours to train on unseen classes. There are many
cases where we have large amount of image-level labels available for training
but cannot be utilized by few shot object detection models for training. There
is a need for a machine learning framework that can be used for training any
unseen class and can become useful in real-time situations. In this paper, we
proposed an "Unseen Class Detector" that can be trained within a very short
time for any possible unseen class without bounding boxes with competitive
accuracy. We build our approach on "Strong" and "Weak" baseline detectors,
which we trained on existing object detection and image classification
datasets, respectively. Unseen concepts are fine-tuned on the strong baseline
detector using only image-level labels and further adapted by transferring the
classifier-detector knowledge between baselines. We use semantic as well as
visual similarities to identify the source class (i.e. Sheep) for the
fine-tuning and adaptation of unseen class (i.e. Goat). Our model (UnseenNet)
is trained on the ImageNet classification dataset for unseen classes and tested
on an object detection dataset (OpenImages). UnseenNet improves the mean
average precision (mAP) by 10% to 30% over existing baselines (semi-supervised
and few-shot) of object detection on different unseen class splits. Moreover,
training time of our model is <10 min for each unseen class. Qualitative
results demonstrate that UnseenNet is suitable not only for few classes of
Pascal VOC but for unseen classes of any dataset or web. Code is available at
https://github.com/Asra-Aslam/UnseenNet.
