AirShot: Efficient Few-Shot Detection for Autonomous Exploration

• URL: http://arxiv.org/abs/2404.05069v1
• Date: Sun, 7 Apr 2024 20:39:31 GMT
• Title: AirShot: Efficient Few-Shot Detection for Autonomous Exploration
• Authors: Zihan Wang, Bowen Li, Chen Wang, Sebastian Scherer,
• Abstract summary: 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. Existing methods still face performance and efficiency challenges, mainly due to unreliable features and exhaustive class loops. We propose a new paradigm AirShot, and discover that, by fully exploiting the valuable correlation map, AirShot can result in a more robust few-shot object detection system.
• Score: 18.09990606406497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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