  Aiming at recognizing and localizing the object of novel categories by a few
reference samples, few-shot object detection (FSOD) is a quite challenging
task. Previous works often depend on the fine-tuning process to transfer their
model to the novel category and rarely consider the defect of fine-tuning,
resulting in many application drawbacks. For example, these methods are far
from satisfying in the episode-changeable scenarios due to excessive
fine-tuning times, and their performance on low-quality (e.g., low-shot and
class-incomplete) support sets degrades severely. To this end, this paper
proposes an instant response few-shot object detector (IR-FSOD) that can
accurately and directly detect the objects of novel categories without the
fine-tuning process. To accomplish the objective, we carefully analyze the
defects of individual modules in the Faster R-CNN framework under the FSOD
setting and then extend it to IR-FSOD by improving these defects. Specifically,
we first propose two simple but effective meta-strategies for the box
classifier and RPN module to enable the object detection of novel categories
with instant response. Then, we introduce two explicit inferences into the
localization module to alleviate its over-fitting to the base categories,
including explicit localization score and semi-explicit box regression.
Extensive experiments show that the IR-FSOD framework not only achieves
few-shot object detection with the instant response but also reaches
state-of-the-art performance in precision and recall under various FSOD
settings.
