result, it combines different relevant clues in order to enhance the discrimination power of the learned detectors and reduce class confusion. We evaluate the proposed method on two challenging few-shot object detection benchmarks, and empirical results show that the proposed method obtains competitive...
86 papers with code • 9 benchmarks • 7 datasets Few-Shot Object Detection is a computer vision task that involves detecting objects in images with limited training data. The goal is to train a model on a few examples of each object class and then use the model to detect objects in...
同年(2019年ICCV上),发表了一篇思路类似但实现略有差异的论文(事实上,meta R-CNN是在该文之后发表的),其名曰《Few-shot Object Detection via Feature Reweighting》,即通过对特征进行重新加权来实现少样本目标检测。 这不禁让我想到,Meta R-CNN不也是这种通过对novel class的特征进行重新加权的方式来突出其重要特...
1、 将K-shot C-way的novel class数据集转换为class-attentive vectors ifself.meta_train:rcnn_loss_cls=[]rcnn_loss_bbox=[]# pooled feature maps need to operate channel-wise multiplication with the corresponding class's attentions of every roi of imageforbinrange(batch_size):zero=Variable(torc...
In this section, we start with the preliminaries on the fewshot object detection setting. Then, we talk about our two stage fine-tuning approach in Section 3.1. Section 3.2 summarizes the previous meta-learning approaches. 在这一节中,我们先介绍一下少数镜头对象检测设置。然后,我们在3.1节中讨论我...
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...
COOPERATING RPN’S IMPROVE FEW-SHOT OBJECTDETECTION 摘要 学习从很少的训练例子中检测图像中的目标是具有挑战性的,因为看到建议框的分类器只有很少的训练数据。当有一个或两个训练例子时,就会出现一个特别具有挑战性的训练方案。在这种情况下,如果区域建议网络(RPN)甚至漏掉一个高相交-联集(IOU)训练框,分类器的...
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...
This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples. While transformer-based open-set detectors, such as DE-ViT, show promise in traditional few-shot object detection, ...
the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, and intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes. Especially, the research in this emerging...