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Few-shot object detection (FSOD) identifies objects from extremely few
annotated samples. Most existing FSOD methods, recently, apply the two-stage
learning paradigm, which transfers the knowledge learned from abundant base
classes to assist the few-shot detectors by learning the global features.
However, such existing FSOD approaches seldom consider the localization of
objects from local to global. Limited by the scarce training data in FSOD, the
training samples of novel classes typically capture part of objects, resulting
in such FSOD methods cannot detect the completely unseen object during testing.
To tackle this problem, we propose an Extensible Co-Existing Attention (ECEA)
module to enable the model to infer the global object according to the local
parts. Essentially, the proposed module continuously learns the extensible
ability on the base stage with abundant samples and transfers it to the novel
stage, which can assist the few-shot model to quickly adapt in extending local
regions to co-existing regions. Specifically, we first devise an extensible
attention mechanism that starts with a local region and extends attention to
co-existing regions that are similar and adjacent to the given local region. We
then implement the extensible attention mechanism in different feature scales
to progressively discover the full object in various receptive fields.
Extensive experiments on the PASCAL VOC and COCO datasets show that our ECEA
module can assist the few-shot detector to completely predict the object
despite some regions failing to appear in the training samples and achieve the
new state of the art compared with existing FSOD methods.