D=S∪Q={IS,MS}∪{IQ,MQ} a. Few-shot Detection Framework 在支持集分支中,首先将图片和掩码拼接起来,一起送进CNN中,提取出特征图,然后进行全局最大池化操作(GMP),得到原型向量v_{ik}=f_{\theta_{S}}\left( I^{S} \oplus M^{S} \right), 原型的计算为\mu_{i} = \frac{1}{K}\sum_{...
文章链接:Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation 代码链接:github.com/ZhangGongjie 出发点 以往的方法在小样本目标检测上还有很大的局限性,表现在: 首先,基于候选框检测的框架很依赖候选框去生成最终的预测,针对新类的低质量候选区域会造成很大的影响。 其次,现...
几篇论文实现代码:《Few-Shot Class-Incremental Learning》(CVPR 2020) GitHub:http://t.cn/A6y2HFAW [fig5] 《SPECTER: Document-level Representation Learning using Citation-informed Transformers》(ACL...
2017) who study the problem of incremental target detection from the perspective of IL. But none of these methods can cope with the situation where the novel target data is few.Perez-Rua et al. (2020)introduced the incremental few-shot object detection (iFSD) paradigm, where new classes are...
本文是一篇关于少量样本增量学习(Few-shot Class-Incremental Learning, FSCIL)的综述,提出了一种新的分类方法,将FSCIL分为五个子类别,并提供了广泛的文献回顾和性能评估,讨论了FSCIL的定义、挑战、相关学习问题以及在计算机视觉领域的应用。 1 介绍 年份:2024 ...
A survey on few-shot class-incremental learning Songsong Tian, ... Prayag Tiwari, in Neural Networks, 2024 4.1.2 Statistical distribution From the statistical distribution perspective, solving the FSCIL problem involves fitting models to existing datasets and predicting the data distribution of the ...
Wireless capsule endoscopy (WCE) is a patient-friendly and non-invasive technology that scans the whole of the gastrointestinal tract, including difficult-to-access regions like the small bowel. Major drawback of this technology is that the visual inspec
Despite the big success of transfer learning techniques in anomaly detection, it is still challenging to achieve good transition of detection rules merely based on the preferred data in the anomaly detection with one-class classification, especially for the data with a large distribution difference. ...
With the rapid development of remote sensing technology and the growing demand for applications, the classical deep learning-based object detection model is bottlenecked in processing incremental data, especially in the increasing classes of detected objects. It requires models to sequentially learn new ...
Few-shot class-incremental learning via entropy-regularized data-free replay. In Computer Vision, Proceedings of ECCV 2022: 17th European Conference, Tel Aviv, Israel, 23–27 October 2022; Springer: Cham, Switzerland, 2022; pp. 146–162. [Google Scholar] Wen, Y.; Zhang, K.; Li, Z.; ...