  In this paper, we present a semi supervised deep quick learning framework for
instance detection and pixel-wise semantic segmentation of images in a dense
clutter of items. The framework can quickly and incrementally learn novel items
in an online manner by real-time data acquisition and generating corresponding
ground truths on its own. To learn various combinations of items, it can
synthesize cluttered scenes, in real time. The overall approach is based on the
tutor-child analogy in which a deep network (tutor) is pretrained for
class-agnostic object detection which generates labeled data for another deep
network (child). The child utilizes a customized convolutional neural network
head for the purpose of quick learning. There are broadly four key components
of the proposed framework semi supervised labeling, occlusion aware clutter
synthesis, a customized convolutional neural network head, and instance
detection. The initial version of this framework was implemented during our
participation in Amazon Robotics Challenge (ARC), 2017. Our system was ranked
3rd, 4th and 5th worldwide in pick, stow-pick and stow task respectively. The
proposed framework is an improved version over ARC17 where novel features such
as instance detection and online learning has been added.
