此外,在训练时,作者设计了一种新的训练策略,进一步提高性能 提供一个新的小样本目标检测模型FSOD(few-shot object detection dataset),共包含1000个类别(800类训练,200类测试) 3.FSOD数据集 虽然现在已经有多个目标检测的数据集,像MSCOCO,PASCAL VOC等,但这些数据集存在以下的问题: 各个数据集的标注体系不同,相同...
1.Semantic Relation Reasoningfor Shot-Stable Few-Shot Object Detection paper:https://openaccess.thecvf.com/content/CVPR2021/html/Li_Few-Shot_Object_Detection_via_Classification_Refinement_and_Distractor_Retreatment_CVPR_2021_paper.html code:None 1.1 Motivation 数据稀缺(data scarcity)是FSOD中的难点。检...
few-shot object detection(小样本目标检测)广泛应用于数据有限的条件下,之前很多团队的研究成果聚焦于小样本种类(categories)的表现,旷视研究团队认为在真实应用场景下,测试样本可能包含任何目标物体,因而检测所有类别(classes)至关重要,这需要小样本检测器能够在没有遗忘的条件下学习新的概念(目标)。旷视团队提出了Retent...
CVPR 2021 FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding,程序员大本营,技术文章内容聚合第一站。
CVPR2020 论文分类下载 「人脸识别+目标检测」 Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector 论文:arxiv.org/abs/1908.0199...近日,CVPR官方终于放出了所有的论文列表,并开放下载。 图灵君为大家整理并下载了【人脸检测/识别/重建】和【目标检测/分割/跟踪】的相关论文,关注本公众号...
<p id="abspara0010" view="all"> Traditional object detectors based on deep learning rely on plenty of labeled samples, which are expensive to obtain. Few-shot object detection (FSOD) attempts to solve this problem, learning detection objects from a few l
In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 13003–13012 Qiao L, Zhao Y, Li Z, Qiu X, Wu J, Zhang C (2021) DeFRCN: decoupled faster R-CNN for few-shot object detection. In: Proceedings of the IEEE/CVF international conference on computer vision...
论文: https://arxiv.org/abs/1812.01866 代码: https://github.com/bingykang/Fewshot_Detection 1.研究背景 深度卷积神经网络最近在目标检测方面的成功很大程度上依赖于大量带有准确边界框标注的训练数据。当标记数据不足时,C
CVPR2020《Frustratingly simple few-shot object detection. 》提出的TFA方法是基于两阶段的fine-tune指出了小样本目标检测改进方面的巨大潜力。 ECCV2020《Multi-scale positive sample refinement for few-shot object detection》提出的MPSR在TFA的基础上研究了小样本尺度分布与原始样本不同的问题,通过图片金字塔和FPN相...
论文题目:RepMet: Representative-based metric learning for classification and few-shot object detection 论文地址:openaccess.thecvf.com/c 任务 训练时每一类别仅给少量的样本,希望在测试时,能准确地检测出给定类别的目标。图1是一种极端情况,大图周围的5张小图代表训练时每一类别仅给出一个样本如partridge,测试...