Dataset Bias 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...
FSIR方法在不同数据集上性能不同,可以用image complexity、intra-concept visual consistency和inter-concept visual similarity来描述不同数据集 不同FSIR方法在不同数据集上性能不同,这与dataset structures和method ability都有关,method ability和dataset structures的characteristics紧密相关 ...
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...
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。
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...
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...
We evaluate our method by doing few-shot image recognition on the ImageNet dataset, which achieves the state-of-the-art classification accuracy on novel categories by a significant margin while keeping comparable performance on the large-scale categories. We also test our method on the MiniImage...
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...
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...
Siamese neural networks for one-shot image recognition(ICML 2015) Matching networks for one shot learning (NIPS 2016) Prototypical networks for few-shot learning (NIPS 2017) ... Optimization-based meta-learning Optimization as a model for few-shot learning (ICLR 2017) ...