相比之下,多标签零镜头学习(multi-label zero-shot learning, ZSL)的任务是在测试时间识别图像中的多个新的未见类别,而在训练过程中没有看到相应的视觉示例。在通用ZSL (GZSL)设置中,测试图像可以同时包含多个可见和不可见类。在大规模的多标签设置中,GZSL是特别具有挑战性的,在这种情况下,一个图像中会出现多个不...
Recently, 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 be shared between the auxiliary and target datasets, and is used to bridge ...
In this paper, for the first time, we investigate and formalise a general framework for multi-label zero-shot learning, addressing the unique challenge therein: how to exploit multi-label correlation at test time with no training data for those classes? In particular, we propose (1) a multi...
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs 论文笔记,程序员大本营,技术文章内容聚合第一站。
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between objects of interests, we propose a framework tha...
《Multi-Label Zero-Shot Learning With Structured Knowledge Graphs》论文笔记 隆大学 论文摘要 本文提出了一个对于multi-label zero-shot学习的深度学习框架,这个框架能够为每个输入的实例预测一到多个unseen class labels。 根据人类在...beliefs。传播完成可以进行多标签分类或ML-ZSL。 模型使用了WordNet,学到了要...
In particular, we are interested in the zero-shot learning scenario where the label correlations on the training set might be significantly different from those on the test set.We propose a max-margin formulation where we model prior label correlations but do not incorporate pairwise label ...
Generalized Zero-Shot Extreme Multi-label Learning Nilesh Gupta, Sakina Bohra, Yashoteja Prabhu, Saurabh Purohit, Manik Varma 2021 Knowledge Discovery and Data Mining|August 2021 Published by ACM Publication Download BibTex Extreme Multi-label Learning (XML) involves assigning the subset of most relev...
This repository contains the implementation of Shared Attention for Multi-label Zero-shot Learning.In this work, we address zero-shot multi-label learning for recognition all (un)seen labels using a shared multi-attention method with a novel training mechanism....
This paper investigates a challenging problem of zero-shot learning in the multi-label scenario (MLZSL), wherein, the model is trained to recognize multiple unseen classes within a sample (e.g., an image) based on seen classes and auxiliary knowledge, e.g., semantic information. Existing me...