Error Threshold of SYK Codes from Strong-to-Weak Parity Symmetry Breaking 12 p. What is the origin of the JWST SMBHs? 11 p. URAvatar: Universal Relightable Gaussian Codec Avatars 28 p. Robust Gaussian Processes via Relevance Pursuit 12 p. EgoMimic: Scaling Imitation Learning via Egocentri...
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...
RepMet是第一个,它包括在训练改进的Faster R-CNN检测器的同时学习类代表向量。密切相关的是,[Pnpdet: Efficient few-shot detection without forgetting via plug-and-play sub-networks]在CenterNet框架内学习原型向量和比例因子。这些向量在检测器的分类头中用作类原型... Attention-based 为了解决基于度量学习的方法...
**Multi-Scale Positive Sample Refinement for Few-Shot Object Detection** - 论文:https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2581_ECCV_2020_paper.php - 代码:https://github.com/jiaxi-wu/MPSR ## 遥感旋转目标检测 **PIoU Loss: Towards Accurate Oriented Object Detection in ...
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
首先介绍一个跨域小样本学习任务(Cross-Domain Few-Shot Learning,CD-FSL), CD-FSL解决的是源域与目标域存在领域差异情况下的小样本学习任务,即集合了小样本学习与跨域两个任务的难点问题:1)源域S与目标域T类别集合完全不同,且目标域T中的类别仅存在少量标注样本,例如1shot,5shot;2)S与T属于两个不同领域,例...
Anomaly detection techniques are employed to identify these attacks and guarantee the normal operation of industrial CPS. However, it is still a challenging problem to cope with scenarios with few labeled samples. In this paper, we propose a few-shot anomaly detection model (FSL-PN) based on ...
Few-shot defect detectionFeature enhancementImage generationHumanmachine intelligenceVisual defect detection, which is pivotal in industrial quality control, often requires extensive datasets for training deeplearning models. However, in industrial environments, the presence of multiple production batches, small...
首先介绍一个跨域小样本学习任务(Cross-Domain Few-Shot Learning,CD-FSL), CD-FSL解决的是源域与目标域存在领域差异情况下的小样本学习任务,即集合了小样本学习与跨域两个任务的难点问题:1)源域S与目标域T类别集合完全不同,且目标域T中的类别仅存在少量标注样本,例如1shot,5shot;2)S与T属于两个不同领域,例...