Semantic Prompt for Few-Shot Image Recognition 原论文于2023.11.6撤稿,原因:缺乏合法的授权,详见此处 Abstract 在小样本学习中(Few-shot Learning, FSL)中,有通过利用额外的语义信息,如类名的文本Embedding,通过将语义原型与视觉原型相结合来解决样本稀少的问题。但这种方法可能会遇到稀有样本中学到噪声特征导致收益有...
数据集image complexity较低时,性能越高。同时,intra-concept visual consistency和inter-concept visual similarity也会影响性能差异 不同方法在不同数据集上性能不同,这和dataset structures、method ability有关。经过实验分析,最好的metric-based方法和meta-learning方法的性能可比。好的metric function对于metric-based方...
in Few-shot Image Recognition-Shuqiang Jiang 主要研究了在少样本图像识别(Few-shot Image Recognition, FSIR)中,数据集偏差对模型性能的影响,并探讨了不同数据集结构和少样本学习方法之间的性能差异。 (1)研究背景:FSIR的目标是利用从训练数据(基础类别)中学习到的可转移知识来识别新类别,通常只需要少量的标注样本。
To evaluate our proposed framework, we have tested it on two image datasets - the large-scale ImageNet and the small-scale but fine-grained CUB-200. We have also studied parameter sensitivity to better understand our framework.doi:10.1007/978-3-030-64559-5_1Debasmit Das...
论文阅读笔记《Few-Shot Image Recognition with Knowledge Transfer》,程序员大本营,技术文章内容聚合第一站。
Few-Shot Image Recognition by Predicting Parameters from Activations Torch implementation for few-shot learning by predicting parameters from activations:Few-Shot Image Recognition by Predicting Parameters from Activations Siyuan Qiao, Chenxi Liu, Wei Shen, Alan Yuille In Conference on Computer Vision and...
早期的 Few-shot Learning 算法研究主要集中在小样本图像识别的任务上,以 MiniImage 和 Omnigraffle 两个数据集为代表。 近年来,在自然语言处理领域也开始出现 Few-shot Learning 的数据集和模型,相比于图像,文本的语义中包含更多的变化和噪声,我们将在本节从数据集和模型两个方面介绍 Few-shot Learning 在自然语言...
To solve this problem, an object detection network and two-stage fine-tuning approach based on You Only Look Once (YOLO)v4 is proposed in this paper to achieve image recognition of electrical equipment under the condition of small samples. Using the two-stage and dual-network method as the ...
论文名称:Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data 原文作者:Ashraful Islam 内容提要 现有的few-shot学习大多依赖于元学习,即在一个大的基本数据集上进行网络学习,而这些数据集通常来自与目标数据集相同的领域。我们解决了在基域和目标域之间存在较大偏移的跨域few-shot...
[1] G Koch, R Zemel, and R Salakhutdinov. Siamese neural networks for one-shot image recognition. In ICML Deep Learning workshop, 2015. [2] Oriol Vinyals, Charles Blundell, Tim Lillicrap, Daan Wierstra, et al. Matching networks for one shot learning. In Advances in Neural Information ...