  One-shot object detection (OSOD) aims to detect all object instances towards
the given category specified by a query image. Most existing studies in OSOD
endeavor to explore effective cross-image correlation and alleviate the
semantic feature misalignment, however, ignoring the phenomenon of the model
bias towards the base classes and the generalization degradation on the novel
classes. Observing this, we propose a novel framework, namely Base-class
Suppression and Prior Guidance (BSPG) network to overcome the problem.
Specifically, the objects of base categories can be explicitly detected by a
base-class predictor and adaptively eliminated by our base-class suppression
module. Moreover, a prior guidance module is designed to calculate the
correlation of high-level features in a non-parametric manner, producing a
class-agnostic prior map to provide the target features with rich semantic cues
and guide the subsequent detection process. Equipped with the proposed two
modules, we endow the model with a strong discriminative ability to distinguish
the target objects from distractors belonging to the base classes. Extensive
experiments show that our method outperforms the previous techniques by a large
margin and achieves new state-of-the-art performance under various evaluation
settings.
