Jump to : Download | Abstract | Contact | BibTex reference | EndNote reference |


Yulei Niu, Zhiwu Lu, Ji-Rong Wen, Tao Xiang, Shih-Fu Chang. Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation. IEEE Transactions on Image Processing, 28(4):1720-1731, 2019.

Download [help]

Download paper: Adobe portable document (pdf)

Copyright notice:This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn a rich feature representation suitable for predicting a diverse set of visual concepts ranging from object, scene to abstract concept; 2) how to annotate an image with the optimal number of class labels. To address the first issue, we propose a novel multi-scale deep model for extracting rich and discriminative features capable of representing a wide range of visual concepts. Specifically, a novel two-branch deep neural network architecture is proposed which comprises a very deep main network branch and a companion feature fusion network branch designed for fusing the multi-scale features computed from the main branch. The deep model is also made multi-modal by taking noisy user-provided tags as model input to complement the image input. For tackling the second issue, we introduce a label quantity prediction auxiliary task to the main label prediction task to explicitly estimate the optimal label number for a given image. Extensive experiments are carried out on two large-scale image annotation benchmark datasets and the results show that our method significantly outperforms the state-of-the-art


Shih-Fu Chang

BibTex Reference

   Author = {Niu, Yulei and Lu, Zhiwu and Wen, Ji-Rong and Xiang, Tao and Chang, Shih-Fu},
   Title = {Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation},
   Journal = {IEEE Transactions on Image Processing},
   Volume = {28},
   Number = {4},
   Pages = {1720--1731},
   Publisher = {IEEE},
   Year = {2019}

EndNote Reference [help]

Get EndNote Reference (.ref)


For problems or questions regarding this web site contact The Web Master.

This document was translated automatically from BibTEX by bib2html (Copyright 2003 © Eric Marchand, INRIA, Vista Project).