  Detecting novel objects from few examples has become an emerging topic in
computer vision recently. However, these methods need fully annotated training
images to learn new object categories which limits their applicability in real
world scenarios such as field robotics. In this work, we propose a
probabilistic multiple instance learning approach for few-shot Common Object
Localization (COL) and few-shot Weakly Supervised Object Detection (WSOD). In
these tasks, only image-level labels, which are much cheaper to acquire, are
available. We find that operating on features extracted from the last layer of
a pre-trained Faster-RCNN is more effective compared to previous episodic
learning based few-shot COL methods. Our model simultaneously learns the
distribution of the novel objects and localizes them via
expectation-maximization steps. As a probabilistic model, we employ von
Mises-Fisher (vMF) distribution which captures the semantic information better
than Gaussian distribution when applied to the pre-trained embedding space.
When the novel objects are localized, we utilize them to learn a linear
appearance model to detect novel classes in new images. Our extensive
experiments show that the proposed method, despite being simple, outperforms
strong baselines in few-shot COL and WSOD, as well as large-scale WSOD tasks.
