  Weakly-supervised object detection (WOD) is a challenging problems in
computer vision. The key problem is to simultaneously infer the exact object
locations in the training images and train the object detectors, given only the
training images with weak image-level labels. Intuitively, by simulating the
selective attention mechanism of human visual system, saliency detection
technique can select attractive objects in scenes and thus is a potential way
to provide useful priors for WOD. However, the way to adopt saliency detection
in WOD is not trivial since the detected saliency region might be possibly
highly ambiguous in complex cases. To this end, this paper first
comprehensively analyzes the challenges in applying saliency detection to WOD.
Then, we make one of the earliest efforts to bridge saliency detection to WOD
via the self-paced curriculum learning, which can guide the learning procedure
to gradually achieve faithful knowledge of multi-class objects from easy to
hard. The experimental results demonstrate that the proposed approach can
successfully bridge saliency detection and WOD tasks and achieve the
state-of-the-art object detection results under the weak supervision.
