  Several object detection methods have recently been proposed in the
literature, the vast majority based on Deep Convolutional Neural Networks
(DCNNs). Such architectures have been shown to achieve remarkable performance,
at the cost of computationally expensive batch training and extensive labeling.
These methods have important limitations for robotics: Learning solely on
off-line data may introduce biases (the so-called domain shift), and prevents
adaptation to novel tasks. In this work, we investigate how weakly-supervised
learning can cope with these problems. We compare several techniques for
weakly-supervised learning in detection pipelines to reduce model (re)training
costs without compromising accuracy. In particular, we show that diversity
sampling for constructing active learning queries and strong positives
selection for self-supervised learning enable significant annotation savings
and improve domain shift adaptation. By integrating our strategies into a
hybrid DCNN/FALKON on-line detection pipeline [1], our method is able to be
trained and updated efficiently with few labels, overcoming limitations of
previous work. We experimentally validate and benchmark our method on
challenging robotic object detection tasks under domain shift.
