Existing meta-learning techniques are powerful in low-shotrecognition, whereas their successes are mostly based on recognizing a single object.Given an image with multi-object information merged in background, they almost fail as the meta-optimization could not disentangle this complex information. 也...
代码见:https//github.com/MegviiDetection/FSCE 现代卷积神经网络(CNN)[1, 2, 3]的发展使一般目标检测有了很大的进步[4, 5, 6]。深度检测器需要大量的标注训练数据以使其性能达到饱和[7, 8]。在few-shot的学习场景中,深度检测器遭受更严重的过拟合,few-shot检测和一般目标检测之间的差距比few-shot图像分类...
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,CNNs会严重过度拟合而不能泛化。计算机视觉系统需要从少量样本中进行检测的学习能力,因为一些对象类别天生就样本稀缺,或者很难获得它们的...
few-shot object detection has received far less attention. Unlike image classification, object detection requires the model to not only recognize the object types but also localize the targets among millions of potential regions. This additional subtask substantially raises the overall complexity. Several...
https://github.com/MegviiDetection/FSCE Emerging interests have been brought to recognize previously unseen objects given very few training examples, known as few-shot object detection (FSOD). Recent researches demonstrate that good feature embedding is the key to reach favorable few-shot learning per...
标题:Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector 这篇文章主要是针对目前大多数跨域小样本学习方法均集中于研究分类任务而忽略了目标检测,因而提出了研究跨域小样本物体检测任务, 文章中提出了一个用于算法评测的CD-FSOD数据集及用于衡量领域差异的style、ICV、IB数据集指标,对现有...
github https://github.com/LiuXinyu12378/few-shot-learning-for-object-detection train.py from __future__ import print_function import sys import time i
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...
几篇论文实现代码:《Few-shot learning with noisy labels》(CVPR 2022) GitHub: github.com/facebookresearch/noisy_few_shot《GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs》(NeurIPS 2022) GitHub: github.com/Emiyalzn/GraphDE [fig9]...
Meta Transfer Learning:这个资源库包含了TensorFlow和PyTorch实现的Meta-Transfer Learning for Few-Shot Learning。 Few Shot:该资源库包含干净的、可读的和经过测试的代码,用于重现几率学习研究。 Few-Shot Object Detection(FsDet):包含官方的简单小样本对象检测的实现。