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...
Few-shot object detection~(FSOD), which aims to detect novel objects with limited annotated instances, has made significant progress in recent years. However, existing methods still suffer from biased representations, especially for novel classes in extremely low-shot scenarios. During fine-tuning, a...
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...
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...
文章通过实验证明了小目标样本的精度下降主要是检测器把小样本目标(novel instances)误分类成了易混淆的基类目标(base instances),因此也提出了通过对比建议编码的小样本目标检测方法FSCE(Few-Shot object detection via Contrastive proposals Encoding),这是一种简单而有效的学习对比感知proposal编码的方法,有助于提升目标...
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。
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...
代码:https://github.com/ucbdrive/few-shot-object-detection Abstract Detecting rare objects from a few examples is an emerging problem. Prior works show meta learning is a promising approach. But, fine-tuning techniques have drawn scant attention. We find that fine-tuning only the last layer of...
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...
(IoU) for an object are naturally analogous to the intra-image augmentation cropping, as illustrated in Figure 1. Therefore in this work, we explore to extend the supervised batch contrastive approach [34] to few-shot object detection. We believe the contrastively learned object representations ...