文章链接:Few-shot Object Detection via Feature Reweighting 代码地址:github.com/bingykang/Fe 1.本文创新点: 小样本检测问题的开山之作。在单阶段目标检测器YOLOv2中加入元特征学习器与 reweighting 模块,解决小样本检测问题。 2.核心思想: 特征提取器提取元特征,元特征要能够泛化到不同类别物体的检测。reweighting...
2020. Few-shot object detection with attention-RPN and multirelation detector. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20). 4012–4021. 2.3 标准化 文章提出目前最常用的用两种标准。 (1)VOC-07/12: 基于VOC07和VOC12数据集,在VOC07训练集...
获得一个few-shot的检测模型对许多应用都是有用的。然而,目前任然缺乏有效的方法。最近,元学习为类似的问题提供了很多可行的解决方案。但是目前的一些模型都是用于few-shot分类,而目标检测在本质上要困难得多,因为它不仅涉及到类的预测,还涉及到目标的定位,因此现成的few-shot分类方法不能直接应用于few-shot检测问题...
Few-shot object detection. There are several early attempts at few-shot object detection using meta-learning.Kang (2019) and Yan apply featurere-weighting schemes to a single-stage object detector (YOLOv2) and a two-stage object detector (Faster R-CNN), with the help of a meta learner tha...
Fractal objectGradient combinationYOLOv7In practical industrial visual inspection tasks, foreign object data are difficult to collect and accumulate, hence few-shot object detection has gradually become the focus of research. It has been observed that industrial foreign objects are often different from ...
元学习技术在FSOD中的应用是通过引入元特征提取器和重新加权模块来优化YOLOv2模型。元特征提取器能够从少量样本中学习通用特征表示，而重新加权模块则进一步调整这些特征以适应特定的检测任务。重新加权模块是该方法的核心，它通过学习权重向量来调整特征表示，以增强模型对不同目标的识别能力。这一过程在学习...
Few-shot object detection.在使用元学习的几样本目标检测方面,有一些早期的尝试。 康et al。(2019)和燕et al .(2019)功能权重方案适用于单级目标检测器(YOLOv2)和两级目标检测器(R-CNN更快),元学习者的帮助下,支持图像(例如,少量的标签图片小说/基类)以及边界框注释作为输入。 Wang等人(2019b)提出了一个权...
We believe that the main reasons that restrict the performance of few-shot detectors are: (1) the positive samples is scarce, and (2) the quality of positive samples is low. Therefore, we put forward a novel few-shot object detector based on YOLOv4, starting from both improving the ...
启发:这篇文章和2019年ICCV的《Few-shot Object Detection via Feature Reweighting》这篇中设计的模型高度相似,区别在于:1. 这篇文章用的是CentreNet,那篇用的是YOLOv2;2. 数据集不一样。 阅读过程中的疑问: 什么是编码-解码模型,怎么生成类通用特征?
论文题目:Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection(用于少镜头目标检测的上下文感知聚合的密集关系蒸馏) 作者:Hanzhe Hu, Shuai Bai, Aoxue Li, Jinshi Cui, Liwei Wang 文献来源:IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) ...