Transductive multi-label zeroshot learning[C]. British Machine Vision Conference ,2015: 1-12.Fu Y, Yang Y, Gong S (2014c) Transductive Multi-label Zero-shot Learning. In: BMVCY. Fu, Y. Yang, T. Hospedales, T. Xiang, and S. Gong. Transductive multi-label zero-shot learning. In ...
Computer Science - LearningComputer Science - Computer Vision and Pattern RecognitionRecently, zero-shot learning (ZSL) has received increasing interest. The key idea underpinning existing ZSL approaches is to exploit knowledge transfer via an intermediate-level semantic representation which is assumed to ...
下图就很简单、形象的展示了Inductive ZSL和Transductive ZSL的训练、测试方法(来源于论文:Transductive Zero-Shot Recognition via Shared Model Space Learning,AAAI, 2016) 但在优化阶段,因为unseen类是没有label的,所以不能像seen类那样通过简单的分类误差进行优化。下面简单提一下对unseen类优化的几种方法: ---1、...
Zhejiang University, Hangzhou, China 2Arizona State University, Tempe, USA 3Alibaba Group, Hangzhou, China Abstract Most existing Zero-Shot Learning (ZSL) methods have the strong bias problem, in which instances of unseen (tar- get) classes tend to be categorized as one of the seen (source)...
【论文学习笔记】Transductive Unbiased Embedding for Zero-Shot Learning (2018_CVPR),程序员大本营,技术文章内容聚合第一站。
为了识别未见类的对象,大多数现有的零样本学习(Zero-Shot Learning, ZSL)方法首先根据源可见类的数据学习公共语义空间和视觉空间之间的兼容投影函数,然后将其直接应用于目标未见类。然而,对于野外的数据(in-the-wild data),源域和目标域之间的分布可能无法很好的匹配,从而导致众所周知的域偏移问题(Domain Shift Proble...
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...
Most zero-shot learning (ZSL) methods based on generative adversarial networks (GANs) utilize random noise and semantic descriptions to synthesize image features of unseen classes, which alleviates the problem of training data imbalance between seen and unseen classes. However, these methods usually onl...
Pool-based active learning is a paradigm where users (e.g., domains experts) are iteratively asked to label initially unlabeled data, e.g., to train a clas... T Reitmaier,A Calma,B Sick - 《Information Sciences》 被引量: 29发表: 2015年 Transductive Zero-Shot Learning With a Self-Trai...
In zero-shot learning (ZSL) community, it is generally recognized that transductive learning performs better than inductive one as the unseen-class samples are also used in its training stage. How to generate pseudo labels for unseen-class samples and how to use such usually noisy pseudo labels...