大多数的现有方法均集中于研究分类问题,即Cross-Domain Few-Shot Classification, 但是同样很重要的物体检测任务(Object Detection,OD)却很少被研究,这促使了研究团队想要探究OD问题在跨域小样本的情况下是否也会遭遇挑战,以及是否会存在跟分类任务表现出不同的特性。 与CD-FSL是FSL在跨域下的分支类似,跨域小样本物体检...
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...
大多数的现有方法均集中于研究分类问题,即Cross-Domain Few-Shot Classification, 但是同样很重要的物体检测任务(Object Detection,OD)却很少被研究,这促使了我们想要探究OD问题在跨域小样本的情况下是否也会遭遇挑战,以及是否会存在跟分类任务表现出不同的特性。 与CD-FSL是FSL在跨域下的分支类似,跨域小样本物体检测(...
大多数的现有方法均集中于研究分类问题,即Cross-Domain Few-Shot Classification, 但是同样很重要的物体检测任务(Object Detection,OD)却很少被研究,这促使了我们想要探究OD问题在跨域小样本的情况下是否也会遭遇挑战,以及是否会存在跟分类任务表现出不同的特性。 与CD-FSL是FSL在跨域下的分支类似,跨域小样本物体检测(...
ECCV24工作讲解视频:title: Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detection Paper: https://arxiv.org/pdf/2402.03094Project Page: http://yuqianfu.com/CDFSOD-benchmark/Data & C, 视频播放量 710、弹幕量 0、点赞数 40、投硬币
该领域的一项开创性工作是[Few-shot object detection via feature reweighting],它训练了一个重加权模块和一个YOLO检测器。重新加权模块通过支持集中的全局池化(GP)输出特定于类的特征向量。然后将它们与主干提取的查询特征进行通道相乘... 上表总结了这篇文献分析。该表旨在比较注意力机制,因为这是本文的重点。因此...
Remote Sensing Object Detection in the Deep Learning Era—A Review. Remote Sens. 2024, 16, 327. [Google Scholar] [CrossRef] Ben Saad, A.; Facciolo, G.; Davy, A. On the Importance of Large Objects in CNN Based Object Detection Algorithms. In Proceedings of the IEEE/CVF Winter ...
The emergence of few-shot object detection provides a new approach to address the challenge of poor generalization ability due to data scarcity. Currently, extensive research has been conducted on few-shot object detection in natural scene datasets, and notable progress has been made. However, in ...
FsDet is well-modularized so you can easily add your own datasets and models. The goal of this repository is to provide a general framework for few-shot object detection that can be used for future research. If you find this repository useful for your publications, please consider citing our...
3.2、Few-Shot Object Detection with Hallucination 我们引入了一个带有参数φ的幻觉网络H,它通过利用基类的共享类内特征变化来学习为新类生成额外的例子。如图4所示,幻觉发生在RoI头部特征空间。幻觉者将可用的训练示例作为输入,并生成幻觉示例。然后,幻觉样本集Sgen被当作额外的训练样本,用于学习新类的分类器。特别地...