  Though quite challenging, leveraging large-scale unlabeled or partially
labeled data in learning systems (e.g., model/classifier training) has
attracted increasing attentions due to its fundamental importance. To address
this problem, many active learning (AL) methods have been proposed that employ
up-to-date detectors to retrieve representative minority samples according to
predefined confidence or uncertainty thresholds. However, these AL methods
cause the detectors to ignore the remaining majority samples (i.e., those with
low uncertainty or high prediction confidence). In this work, by developing a
principled active sample mining (ASM) framework, we demonstrate that
cost-effectively mining samples from these unlabeled majority data is key to
training more powerful object detectors while minimizing user effort.
Specifically, our ASM framework involves a switchable sample selection
mechanism for determining whether an unlabeled sample should be manually
annotated via AL or automatically pseudo-labeled via a novel self-learning
process. The proposed process can be compatible with mini-batch based training
(i.e., using a batch of unlabeled or partially labeled data as a one-time
input) for object detection. In addition, a few samples with low-confidence
predictions are selected and annotated via AL. Notably, our method is suitable
for object categories that are not seen in the unlabeled data during the
learning process. Extensive experiments clearly demonstrate that our ASM
framework can achieve performance comparable to that of alternative methods but
with significantly fewer annotations.
