@inproceedings{aec8ff48a16d4853b8cd871b05bdc59c,
title = "Deep multi-context Network for fine-grained visual recognition",
abstract = "In this paper, we tackle the FINE-GRAINED VISUAL RECOGNITION problem by proposing a deep multi-context framework. We employ deep Convolutional Neural Networks to model features of objects in images. Global context and local context are both taken into consideration, and are jointly modeled in a unified multi-context deep learning framework. To cleanse the relatively dirty data for training, a regional proposal method is designed to make the multi-context modeling suited for fine-grained visual recognition in the real world. Furthermore, recently proposed contemporary deep models are used, and their combination is investigated. Our approaches are evaluated on MSR-IRC 2016 and further assessed on the more complex validation set. The results show significant and consistent improvements over the baseline.",
keywords = "Fine-Grained, Multi-Context, Multi-Model, Object Proposal with Multi-Crop",
author = "Xinyu Ou and Zhen Wei and Ling Hefei and Liu Si and Cao Xiaochun",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016 ; Conference date: 11-07-2016 Through 15-07-2016",
year = "2016",
month = sep,
day = "22",
doi = "10.1109/ICMEW.2016.7574666",
language = "英语",
series = "2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016",
address = "美国",
}