mlsanityData to 7 Oct 2026

AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration

arXiv 2112.01740 ↗First posted 3 Dec 2021cs.CVcited by 12

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Abstract (arXiv)

Few-shot object detection has attracted increasing attention and rapidly progressed in recent years. However, the requirement of an exhaustive offline fine-tuning stage in existing methods is time-consuming and significantly hinders their usage in online applications such as autonomous exploration of low-power robots. We find that their major limitation is that the little but valuable information from a few support images is not fully exploited. To solve this problem, we propose a brand new architecture, AirDet, and surprisingly find that, by learning class-agnostic relation with the support images in all modules, including cross-scale object proposal network, shots aggregation module, and localization network, AirDet without fine-tuning achieves comparable or even better results than many fine-tuned methods, reaching up to 30-40% improvements. We also present solid results of onboard tests on real-world exploration data from the DARPA Subterranean Challenge, which strongly validate the feasibility of AirDet in robotics. To the best of our knowledge, AirDet is the first feasible few-shot detection method for autonomous exploration of low-power robots. The code and pre-trained models are released at https://github.com/Jaraxxus-Me/AirDet.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 36 of its 45 references that are in the map. First posted on arXiv 3 Dec 2021.

Builds on

36 of 45 references.

References · 36

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24 more
  1. 2019LVIS: A Dataset for Large Vocabulary Instance Segmentationcited by 1,215
  2. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  3. 2018SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networkscited by 445
  4. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  5. 2020Frustratingly Simple Few-Shot Object Detectioncited by 280
  6. 2018Few-shot Object Detection via Feature Reweightingcited by 265
  7. 2019Few-Shot Object Detection with Attention-RPN and Multi-Relation Detectorcited by 206
  8. 2019Meta R-CNN : Towards General Solver for Instance-level Few-shot Learningcited by 182
  9. 2021FSCE: Few-Shot Object Detection via Contrastive Proposal Encodingcited by 168
  10. 2020Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wildcited by 142
  11. 2020Multi-Scale Positive Sample Refinement for Few-Shot Object Detectioncited by 116
  12. 2021DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detectioncited by 106
  13. 2021Generalized Few-Shot Object Detection without Forgettingcited by 88
  14. 2021Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detectioncited by 79
  15. 2018LSTD: A Low-Shot Transfer Detector for Object Detectioncited by 77
  16. 2021Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detectioncited by 53
  17. 2021Hallucination Improves Few-Shot Object Detectioncited by 50
  18. 2016HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detectioncited by 46
  19. 2021Universal-Prototype Enhancing for Few-Shot Object Detectioncited by 43
  20. 2017FSSD: Feature Fusion Single Shot Multibox Detectorcited by 42
  21. 2017DSOD: Learning Deeply Supervised Object Detectors from Scratchcited by 41
  22. 2021Few-Shot Object Detection via Association and DIscriminationcited by 39
  23. 2020Visual Memorability for Robotic Interestingness via Unsupervised Online Learningcited by 8
  24. 2021Unsupervised Online Learning for Robotic Interestingness with Visual Memorycited by 5

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 4; 2024: 2; 2025: 2.

12 papers.

  1. 2022FS-DETR: Few-Shot DEtection TRansformer with prompting and without re-trainingcited by 20
  2. 2023Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentationcited by 12
  3. 2024AirShot: Efficient Few-Shot Detection for Autonomous Explorationcited by 10
  4. 2023VoxDet: Voxel Learning for Novel Instance Detectioncited by 8
  5. 2021A Survey of Deep Learning for Low-Shot Object Detectioncited by 6
  6. 2022SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Networkcited by 6
  7. 2025Multi-Perspective Data Augmentation for Few-shot Object Detectioncited by 6
  8. 2022Proposal Distribution Calibration for Few-Shot Object Detectioncited by 2
  9. 2023TIDE: Test Time Few Shot Object Detectioncited by 2
  10. 2025Few-Shot Learning in Video and 3D Object Detection: A Surveycited by 2
  11. 2023Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Explorationcited by 1
  12. 2024Detect an Object At Once without Fine-tuningcited by 1