AirShot: Efficient Few-Shot Detection for Autonomous Exploration
<|reference_start|>AirShot: Efficient Few-Shot Detection for Autonomous Exploration: 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.<|reference_end|>
@article{wang2024airshot:,
    title={AirShot: Efficient Few-Shot Detection for Autonomous Exploration},
    author={Zihan Wang, Bowen Li, Chen Wang, and Sebastian Scherer},
    journal={arXiv preprint arXiv:2404.05069},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05069},
    primaryClass={cs.CV}
}