AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration
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.
Code (1)
Tasks
Few-Shot Object DetectionMeta-Learningobject-detectionObject DetectionSimilar Papers 제목 키워드 기반
PairDETR : Joint Detection and Association of Human Bodies and Faces
Image and video analysis requires not only accurate object but also the understanding of relationships among detected objects. Common solutions to relation modeling typically resort to stand-alone object detectors fo…
Objectobject-detectionObject DetectionFew-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning
In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar. We present a simple yet effective few-shot intent detection schema via contras…
Contrastive LearningIntent DetectionHow Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?
Recent large vision-language models such as CLIP have shown remarkable out-of-distribution (OOD) detection and generalization performance. However, their zero-shot in-distribution (ID) accuracy is often limited for downs…
Out-of-Distribution DetectionOut of Distribution (OOD) DetectionPrompt LearningIncremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning
Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes. Most related prior works are on increme…
Few-Shot Object DetectionKnowledge DistillationObjectobject-detection+2Cos R-CNN for Online Few-shot Object Detection
We propose Cos R-CNN, a simple exemplar-based R-CNN formulation that is designed for online few-shot object detection. That is, it is able to localise and classify novel object categories in images with few examples with…
Few-Shot Object DetectionObjectobject-detectionObject Detection