Class-Incremental Few-Shot Object Detection
Conventional detection networks usually need abundant labeled training samples, while humans can learn new concepts incrementally with just a few examples. This paper focuses on a more challenging but realistic class-incremental few-shot object detection problem (iFSD). It aims to incrementally transfer the model for novel objects from only a few annotated samples without catastrophically forgetting the previously learned ones. To tackle this problem, we propose a novel method LEAST, which can transfer with Less forgetting, fEwer training resources, And Stronger Transfer capability. Specifically, we first present the transfer strategy to reduce unnecessary weight adaptation and improve the transfer capability for iFSD. On this basis, we then integrate the knowledge distillation technique using a less resource-consuming approach to alleviate forgetting and propose a novel clustering-based exemplar selection process to preserve more discriminative features previously learned. Being a generic and effective method, LEAST can largely improve the iFSD performance on various benchmarks.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringFew-Shot Object DetectionKnowledge DistillationObjectobject-detectionObject DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Incremental-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+2Few-Shot Batch Incremental Road Object Detection via Detector Fusion
Incremental few-shot learning has emerged as a new and challenging area in deep learning, whose objective is to train deep learning models using very few samples of new class data, and none of the old class data. In this…
Few-Shot Learningobject-detectionObject DetectionTowards Generalized and Incremental Few-Shot Object Detection
Real-world object detection is highly desired to be equipped with the learning expandability that can enlarge its detection classes incrementally. Moreover, such learning from only few annotated training samples further …
Autonomous DrivingContinual LearningFew-Shot LearningFew-Shot Object Detection+3Incremental Few-Shot Object Detection
Most existing object detection methods rely on the availability of abundant labelled training samples per class and offline model training in a batch mode. These requirements substantially limit their scalability to open…
Few-Shot LearningFew-Shot Object DetectionIncremental LearningMeta-Learning+3Incrementally Zero-Shot Detection by an Extreme Value Analyzer
Human beings not only have the ability to recognize novel unseen classes, but also can incrementally incorporate the new classes to existing knowledge preserved. However, zero-shot learning models assume that all seen cl…
class-incremental learningClass Incremental LearningIncremental Learningobject-detection+2