.png){kind=icon}
返回
Webly-supervised zero-shot learning for artwork instance recognition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2019.09.027.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper describes experiments on supervised approaches to webly-labeled artwork instance recognition and zero-shot learning for unseen artwork instance recognition. We build on our earlier work on webly-supervised learning using the NoisyArt dataset. The dataset consists of more than 90,000 images and in more than 3,000 webly-supervised classes, and a subset of 200 classes with verified test images. Document embeddings are provided for short descriptions of all artworks. NoisyArt is designed to support research on webly-supervised artwork instance recognition, zero-shot learning, and other approaches to visual recognition of cultural heritage objects. We report results of experiments on artwork instance recognition using the NoisyArt dataset of webly-labeled images as well as on the CMU-Oxford Sculptures dataset. In addition, we perform extensive experiments on zero-shot learning using webly-labeled training images for unseen artwork recognition. Our results demonstrate the benefits and limitations of zero-shot learning for instance recognition over webly-supervised data. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
MULTIMEDIA
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
7.9K
被引数:
1.6W
机构
引用论文
Cultural tourism: An analysis of engagement, cultural contact, memorable tourism experience and destination loyalty.png)
tourism management
tourism managementIF6.9
PREAI
分享收藏分享收藏 没有更多内容
没有更多内容