2.2 《One-Shot Object Detection with Co-Attention and Co-Excitation》解读 与之前解析的范式(如Meta RCNN)相似,本文提出了面向于One-shot任务的CoAE,其本质还是按照参考图片(support image)提供的物体,在询问图(query image)中找到同类别的物体。 但在本文中,作者将support image记做query image,将query image ...
In recent years, deep learning has shown promising results in change detection (CD) of hyperspectral images (HSIs). However, these algorithms often require a large number of labeled samples, which can be costly in practical applications. This article proposes a few-shot HSI CD method based on...
Feature Pyramid Network for Object Detection 论文笔记 Feature Pyramid Network for Object Detection 论文链接: https://arxiv.org/abs/1612.03144 一、Problem Statement 特征金字塔在识别系统中是一个基本的组成部分,用于在不同尺度上检测目标。但是因为传统的特征金字塔计算和存储花费太大,作者就提出以一个较小的额...
We demonstrate few-shot learning can effectively handle the diverse perspectives in remote sensing data. As an example application, we evaluate state-of-the-art approaches on a UAV disaster scene dataset, yielding promising results. Furthermore, we highlight the significance of incorporating ...
The detection part of mmfewshot is built upon the mmdet, thus it is highly recommended learning the basic of mmdet. Modify a config through script arguments When submitting jobs using "tools/train.py" or "tools/test.py", you may specify --cfg-options to in-place modify the config. ...
incremental few-shot learning论文阅读 incremental few-shot learning 论文的主要目的:在不使用原始训练数据的情况下,对新增加类别的少量数据进行训练以进行增量学习。 Related Work: object detection 作者比较了一阶段和二阶段检测模型,并讲述了一般检测模型的不足。检测模型需要大量有标注的数据进行训练,当线上部署的...
Existing detection models used massive amounts of data to complete abnormal traffic detection. However, few-shot attack samples can only be intercepted in certain special scenarios. In addition, the discrimination of traffic attributes will also be affected by the change of feature attitude. But the...
When testing other than ImageNet OOD Benchmark,change the value of the training "-topk" and "-lambda" argument and report the value in the paper.The current config is for ImageNet-1K. Let's build a better Few-Shot OOD Detection community together!
To address the above issues, we propose a simple but effective double-branch attention guided few-shot PCB defect detection (FSPDD) model, which is a single-stage model that avoids the influence of quality of proposals unlike two-stage methods. First, we propose a joint attention enhancement ...
In underwater acoustic target recognition, there is a lack of massive high-quality labeled samples to train robust deep neural networks, and it is difficult to collect and annotate a large amount of base class data in advance unlike the image recognition