Object detectionIncremental learning41A0541A1065D0565D17The human vision and perception system is inherently incremental where new knowledge is continually learned over time whilst existing knowledge is retained. On the other hand, deep learning networks are ill-equipped for incremental learning. When a...
我们在评估部分评估并比较了两种实现方式。 4 Incremental learning algorithm 4.1 Recap for One-stage Object Detector 对象检测的目的是识别一组预定义的对象类别(例如人,汽车,自行车,动物)的实例,并使用边界框描述图像中每个检测到的对象的位置。较早的端到端深度学习方法采用两阶段体系结构,该体系结构首先使用区域...
Modeling Missing Annotations for Incremental Learning in Object DetectionFabio Cermelli 1,2 , Antonino Geraci 1 , Dario Fontanel 1 , Barbara Caputo 11 Politecnico di Torino, 2 Italian Institute of Technologyfabio.cermelli@polito.itAbstractDespite the recent advances in the f i eld of object detec...
另外Experience Replay for Continual Learning (NIPS 2019)指出这类模型可以动态调整旧数据的保留数量,从而避免了LwF算法随着任务数量的增大,计算成本线性增长的缺点。基于iCaRL算法的一些有影响力的改进算法包括End-to-End Incremental Learning (ECCV 2018)和Large Scale Incremental Learning (CVPR 2019),这些模型的损失...
A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks[22] 目标检测 Incremental Few-Shot Object Detection (CVPR 2020)[23] Incremental Learning of Object Detectors without Catastrophic Forgetting (ICCV 2017)[24] 语义分割 ...
增量学习和持续学习(Continual Learning)、终身学习(Lifelong Learning)的概念大致是等价的,它们都是在连续的数据流中训练模型,随着时间的推移,更多的数据逐渐可用,同时旧数据可能由于存储限制或隐私保护等原因而逐渐不可用,并且学习任务的类型和数量没有预定义(例如分类任务中的类别数)。
A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks[22] 目标检测 Incremental Few-Shot Object Detection (CVPR 2020)[23] Incremental Learning of Object Detectors without Catastrophic Forgetting (ICCV 2017)[24] 语义分割 ...
A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks[22] 目标检测 Incremental Few-Shot Object Detection (CVPR 2020)[23] Incremental Learning of Object Detectors without Catastrophic Forgetting (ICCV 2017)[24] 语义分割 ...
In this work, we propose three incremental learning scenarios across various domains and categories for object detection. To mitigate catastrophic forgetting, attentive feature distillation is proposed to leverages both bottom-up and top-down attentions to extract important information for distillation. We...
Few-shot Class-Incremental Learning (FSCIL) presents a unique challenge in Machine Learning (ML), as it necessitates the Incremental Learning (IL) of new classes from sparsely labeled training samples without forgetting previous knowledge. While this field has seen recent progress, it remains an ...