PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
Abstract-Weakly Supervised Object Detection(WSOD), using only image-level annotations to
train object detectors, is of growing importance in object recognition. In this paper, we
propose a novel deep network for WSOD. Unlike previous networks that transfer the object
detection problem to an image classification problem using Multiple Instance Learning(MIL)
our strategy generates proposal clusters to learn refined instance classifiers by an
iterative process. The proposals in the same cluster are spatially adjacent and associated
with the same object. This prevents the networks from concentrating too much on parts of
objects instead of whole objects. We first show that instances can be assigned object or
background labels directly based on proposal clusters for instance classifier refinement,
and then show that treating each cluster as a small new bag yields fewer ambiguities than
the directly assigning label method. The iterative instance classifier refinement is
implemented online using multiple streams in convolutional neural networks, where the
first is an MIL network and the others are for instance classifier refinement supervised
by the preceding one. Experiments are conducted on the PASCAL VOC, ImageNet detection, and
MS-COCO benchmarks for WSOD. Results show that our method outperforms the previous state
of the art significantly.
Index Terms-Object detection, weakly supervised learning, convolutional neural network,
multiple instance learning, proposal cluster.