  The labeling cost of large number of bounding boxes is one of the main
challenges for training modern object detectors. To reduce the dependence on
expensive bounding box annotations, we propose a new semi-supervised object
detection formulation, in which a few seed box level annotations and a large
scale of image level annotations are used to train the detector. We adopt a
training-mining framework, which is widely used in weakly supervised object
detection tasks. However, the mining process inherently introduces various
kinds of labelling noises: false negatives, false positives and inaccurate
boundaries, which can be harmful for training the standard object detectors
(e.g. Faster RCNN). We propose a novel NOise Tolerant Ensemble RCNN (NOTE-RCNN)
object detector to handle such noisy labels. Comparing to standard Faster RCNN,
it contains three highlights: an ensemble of two classification heads and a
distillation head to avoid overfitting on noisy labels and improve the mining
precision, masking the negative sample loss in box predictor to avoid the harm
of false negative labels, and training box regression head only on seed
annotations to eliminate the harm from inaccurate boundaries of mined bounding
boxes. We evaluate the methods on ILSVRC 2013 and MSCOCO 2017 dataset; we
observe that the detection accuracy consistently improves as we iterate between
mining and training steps, and state-of-the-art performance is achieved.
