  Few-shot object detection has drawn increasing attention in the field of
robotic exploration, where robots are required to find unseen objects with a
few online provided examples. Despite recent efforts have been made to yield
online processing capabilities, slow inference speeds of low-powered robots
fail to meet the demands of real-time detection-making them impractical for
autonomous exploration. Existing methods still face performance and efficiency
challenges, mainly due to unreliable features and exhaustive class loops. In
this work, we propose a new paradigm AirShot, and discover that, by fully
exploiting the valuable correlation map, AirShot can result in a more robust
and faster few-shot object detection system, which is more applicable to
robotics community. The core module Top Prediction Filter (TPF) can operate on
multi-scale correlation maps in both the training and inference stages. During
training, TPF supervises the generation of a more representative correlation
map, while during inference, it reduces looping iterations by selecting
top-ranked classes, thus cutting down on computational costs with better
performance. Surprisingly, this dual functionality exhibits general
effectiveness and efficiency on various off-the-shelf models. Exhaustive
experiments on COCO2017, VOC2014, and SubT datasets demonstrate that TPF can
significantly boost the efficacy and efficiency of most off-the-shelf models,
achieving up to 36.4% precision improvements along with 56.3% faster inference
speed. Code and Data are at: https://github.com/ImNotPrepared/AirShot.
