在直推式零样本学习（Transductive Zero-Shot Learning, TZSL）领域，一篇名为“Transductive zero-shot learning with adaptive structural embedding”的论文，由Yu, Ji, Guo等人于2018年在IEEE Transactions on Neural Networks and Learning Sys
TransductiveBidirectional generationCycleGANMost zero-shot learning (ZSL) methods aim to learn a mapping from visual feature space to semantic feature space or from both visual and semantic feature spaces to a common joint space and align them. However, in these methods the visual and semantic ...
Zero-shot learning (ZSL) aims to learn a projection function from a visual feature space to a semantic embedding space or reverse. The main challenge of ZSL is the domain shift problem where the unseen test data has a large gap with the seen training data. Transductive ZSL based methods all...
前段时间在弄关于 Zero-Shot Learning (ZSL)的实验和论文(前天提交出去的,希望这次可以有好的结果,我愿意用身上十斤肉来换,哈哈哈~),看到了一些关于Transductive ZSL的文章,但当我打算开始做这方面的实验…
Zero-shot learning (ZSL) aims to recognize unseen classes during training. Transductive methods have advanced in ZSL, however, often rely on pseudo labels based on confidence scores, leading to semantic misalignment between unseen-class image features and corresponding class semantic descriptions due to...
言归正传,这一篇还是与Zero-shot Learning相关的论文,一个基于自适应结构嵌入(Adaptive Structural Embedding)的直推式ZSL(Transductive Zero-Shot Learning, 后面简称TZSL)的论文。这里先补充几个关于ZSL的概念问题。 1)ZSL:Recognize the unseen categories that no labeled data are available for training, i.e.,...
(Few-shot detection)Review: Transductive Learning for zero-shot object detection,程序员大本营,技术文章内容聚合第一站。
signif icant progress has been made for image recognitionin the past years [ 12 ]. However, it is unrealistic to label all the object classes, thus making thesesupervised learning methods struggle to recognize objects which are unseen during training. Bycontrast, Zero-Shot Learning (ZSL) [ 24...
Zero-shot learning (ZSL) aims to learn a projection function from a visual feature space to a semantic embedding space or reverse. The main challenge of ZSL is the domain shift problem where the unseen test data has a large gap with the seen training data. Transductive ZSL based methods all...
transductive zero-shot and few-shot CLIP classification challenge in which inference is performed jointly across a mini-batch of unlabeled query samples rather than treating each instance independently. We initially construct informative vision-text probability features leading to a classification problem on...