  The current advances in object detection depend on large-scale datasets to
get good performance. However, there may not always be sufficient samples in
many scenarios, which leads to the research on few-shot detection as well as
its extreme variation one-shot detection. In this paper, the one-shot detection
has been formulated as a conditional probability problem. With this insight, a
novel one-shot conditional object detection (OSCD) framework, referred as
Comparison Network (ComparisonNet), has been proposed. Specifically, query and
target image features are extracted through a Siamese network as mapped metrics
of marginal probabilities. A two-stage detector for OSCD is introduced to
compare the extracted query and target features with the learnable metric to
approach the optimized non-linear conditional probability. Once trained,
ComparisonNet can detect objects of both seen and unseen classes without
further training, which also has the advantages including class-agnostic,
training-free for unseen classes, and without catastrophic forgetting.
Experiments show that the proposed approach achieves state-of-the-art
performance on the proposed datasets of Fashion-MNIST and PASCAL VOC.
