  We address the problem of weakly supervised object localization where only
image-level annotations are available for training object detectors. Numerous
methods have been proposed to tackle this problem through mining object
proposals. However, a substantial amount of noise in object proposals causes
ambiguities for learning discriminative object models. Such approaches are
sensitive to model initialization and often converge to undesirable local
minimum solutions. In this paper, we propose to overcome these drawbacks by
progressive representation adaptation with two main steps: 1) classification
adaptation and 2) detection adaptation. In classification adaptation, we
transfer a pre-trained network to a multi-label classification task for
recognizing the presence of a certain object in an image. Through the
classification adaptation step, the network learns discriminative
representations that are specific to object categories of interest. In
detection adaptation, we mine class-specific object proposals by exploiting two
scoring strategies based on the adapted classification network. Class-specific
proposal mining helps remove substantial noise from the background clutter and
potential confusion from similar objects. We further refine these proposals
using multiple instance learning and segmentation cues. Using these refined
object bounding boxes, we fine-tune all the layer of the classification network
and obtain a fully adapted detection network. We present detailed experimental
validation on the PASCAL VOC and ILSVRC datasets. Experimental results
demonstrate that our progressive representation adaptation algorithm performs
favorably against the state-of-the-art methods.
