  To reduce the manpower consumption on box-level annotations, many weakly
supervised object detection methods which only require image-level annotations,
have been proposed recently. The training process in these methods is
formulated into two steps. They firstly train a neural network under weak
supervision to generate pseudo ground truths (PGTs). Then, these PGTs are used
to train another network under full supervision. Compared with fully supervised
methods, the training process in weakly supervised methods becomes more complex
and time-consuming. Furthermore, overwhelming negative proposals are involved
at the first step. This is neglected by most methods, which makes the training
network biased towards to negative proposals and thus degrades the quality of
the PGTs, limiting the training network performance at the second step. Online
proposal sampling is an intuitive solution to these issues. However, lacking of
adequate labeling, a simple online proposal sampling may make the training
network stuck into local minima. To solve this problem, we propose an Online
Active Proposal Set Generation (OPG) algorithm. Our OPG algorithm consists of
two parts: Dynamic Proposal Constraint (DPC) and Proposal Partition (PP). DPC
is proposed to dynamically determine different proposal sampling strategy
according to the current training state. PP is used to score each proposal,
part proposals into different sets and generate an active proposal set for the
network optimization. Through experiments, our proposed OPG shows consistent
and significant improvement on both datasets PASCAL VOC 2007 and 2012, yielding
comparable performance to the state-of-the-art results.
