在细粒度图像领域也有few-shot的需求,在传统的fine grained不管是检索还是识别也好,依赖的数据是非常大的,甚至是海量的标注数据,这样的海量标注数据,fine grained仅仅依靠普通人标注是不行的,由于标注量和成本都是非常大的,所以在fine grained未来的发展上,我们觉得像few-shot这样的场景非常重要,few-shot能使用训练数据...
Few-Shot Learning for Fine-Grained Signal Modulation Recognition Based on Foreground Segmentation 世界和平的使命 为世界快乐和平努力奋斗!1 人赞同了该文章 方法 问题建立:截取固定长度信号上升沿——CWD——构建基准集、支持集、查询集 显著区域分割:分割网络(无监督双DIP)——特征提取和融合网络(融合高阶特征和...
论文分为两个部分, base learner和task embedding. 其中task embedding从实验数据角度来看基本没什么用, 可能主要是限制于最终只作用于classifer(全连接层, 是可以直接算出最优解). 文章优点在于锁定了其研究的问题是细粒度少样本图片识别few-shot fine-grained image recognition (FSFGIR), 从元学习的角度来说并没...
Task Discrepancy Maximization for Fine-grained Few-Shot Classification SuBeen Lee, WonJun Moon, Jae-Pil Heo* Sungkyunkwan University {leesb7426, wjun0830, jaepilheo}@skku.edu Abstract Recognizing discriminative details such as eyes and beaks is important for distinguishin...
FUNIT是一个few-shot学习模型,且不需要paired data,可以将若干个风格字同时提取特征,并整合成一个general的特征表示,再融入到主干生成网络中。它也是一个字形特征+风格特征融合的典型案例,后续很多专门针对字体生成提出的模型也几乎延续了这种模型架构,即使用一个网络提取标准字的字形特征,再用一个网络提取风格字的风...
Few-shot fine-grained visual classification aims to identify fine-grained concepts with very few samples, which is widely used in many fields, such as the classification of different species of birds in biological research, and the identification of car models in traffic monitoring. Compared with ...
Fine-grained few/zero shot learning Fine-grained hashing Fine-grained domain adaptation Fine-grained image generation FGIA within more realistic settings Toolbox Recognition leaderboard IntroductionThis homepage lists some representative papers/codes/datasets all about deep learning based fine-grained...
Fine-grained few shot learning Fine-grained hashing FGIA within more realistic settings Leaderboard 1. Introduction This homepage lists some representative papers/codes/datasets all about deep learning basedfine-grained image, including fine-grained image recognition, fine-grained image retrieval, fine-grai...
Localizing discriminative object parts (e.g., bird head) is crucial for fine-grained classification tasks, especially for the more challenging fine-grained few-shot scenario. Previous work always relies on the learned object parts in a unified manner, where they attend the same object parts (even...
任务定义:采用标准的few-shot学习任务设置,包括5-way 1-shot和5-way 5-shot,也即在每个任务中随机选择5个类别,每个类别分别提供1个或5个样本用于训练(支持集),再使用多个查询样本来测试模型的分类性能。 数据划分:每个数据集被分为训练集、验证集和测试集。训练集用于训练模型,验证集用于调参,测试集用于评估模型...