2. Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection paper:https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Dense_Relation_Distillation_With_Context-Aware_Aggregation_for_Few-Shot_Object_Detection_CVPR_2021_paper.html code:https://github.com/hzhupku/DCNe...
该体系结构可以改善基于dml的目标分类和few-shot object detection的技术现状。 其次,我们提出一种基于DML分类器头的目标检测器方法,该分类器头可以识别新的类别,从而将其转化为 few-shot detector检测器。据我们所知,这是以前没有过的。 第三,在few-shot classification文献中,通常的做法是通过对few-shot task(称...
few-shot object detection,讲解-回复 什么是few-shot目标检测? 目标检测是计算机视觉领域的一项关键任务,其目的是通过算法和模型来识别图像中特定物体的位置和类别。传统的目标检测方法通常依赖于大规模标注数据进行训练,这对于许多应用而言是一种挑战,因为获取大量标注数据是非常耗时和昂贵的。而few-shot目标检测是一...
为了保证few-shot的泛化能力,采用两阶段学习方案对整个few-shot检测模型进行训练:首先从基类中学习元特征和良好的权值调整模块;然后对检测模型进行微调以适应新的类。为了解决检测学习中的困难(例如,存在分散注意力的对象),它引入了一个新的损失函数。 3.方案具体实施 关于数据集 本文针对few-shot目标检测,设置了两种...
In this paper, we study object detection using a large pool of unlabeled images and only a few labeled images per category, named "few-shot object detection". The key challenge consists in generating trustworthy training samples as many as possible from the pool. Using few training examples as...
为了让RPN学习到支持集和查询集之间目标的潜在联系，避免RPN在查询集的未知样本上出现随机游走的情况。为什么池化尺度为 1 VS. Conventional RPN Overlap 0.5：0.9130 vs. 0.8804 ABO(Average Best Overlap Ratio)：0.7282 vs. 0.7127 为什么构建？将支持集和查询集的目标通过全局、局部、块区三个...
Few-Shot Object Detection(FSOD)是计算机视觉中一个快速发展的领域。它包括查找给定类集的所有出现,每个类只有几个带注释的示例。已经提出了许多方法来应对这一挑战,其中大多数是基于注意力机制的。然而,种类繁多的经典目标检测框架和训练策略使得方法之间的性能比较变得困难。
Code README Apache-2.0 license Few-Shot Object Detection (FsDet) FsDet contains the official few-shot object detection implementation of the ICML 2020 paperFrustratingly Simple Few-Shot Object Detection. In addition to the benchmarks used by previous works, we introduce new benchmarks on three da...
Few-shot object detection (FSOD), with the aim to detect novel objects using very few training examples, has recently attracted great research interest in the community. Metric-learning based methods have been demonstrated to be effective for this task using a two-branch based siamese network, an...
Generalized Few-Shot Object Detection without Forgetting 1、摘要 近年来,少样本目标检测被广泛用于处理数据有限的情况。虽然大多数以前的工作仅仅集中在少样本类别的性能上,我们声称检测所有类别是至关重要的,因为测试样本可能包含现实应用中的任何实例,这需要少样本检测器在不忘记的情况下学习新概念。通过对基于迁移...