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Accurate Few-Shot Object Detection With Support-Query Mutual Guidance and Hybrid Loss
Most object detection methods require huge amounts of annotated data and can detect only the categories that appear in the training set. However, in reality acquiring massive annotated training data is both expensive and time-consuming. In this paper, we propose a novel two-stage detector for accurate few-shot object detection. In the first stage, we employ a support-query mutual guidance mechanism to generate more support-relevant proposals. Concretely, on the one hand, a query-guided support weighting module is developed for aggregating different supports to generate the support feature. On the other hand, a support-guided query enhancement module is designed by dynamic kernels. In the second stage, we score and filter proposals via multi-level feature comparison between each proposal and the aggregated support feature based on a distance metric learnt by an effective hybrid loss, which makes the embedding space of distance metric more discriminative. Extensive experiments on benchmark datasets show that our method substantially outperforms the existing methods and lifts the SOTA of FSOD task to a higher level.