@inproceedings{b8083d2ae10f4ea8a199204f7e03bd6e,
title = "Learning semantic embedding at a large scale",
abstract = "A key problem in image annotation is to learn the underlying semantics. However, finding such semantic embeddings is a challenge task and often requires large amount of tagging information. In this paper, we propose to utilize multi-modality cues by incorporating visual and textual information as embedded objects. The paper further presents a multi-task learning framework that simultaneously learns the approximation of two semantic embeddings with efficient multi-stage convex relaxation technique. The experiments show that the proposed method presents very promising performance in both memory usage and training time for large-scale dataset, as well as image classification accuracy.",
keywords = "Semantic embedding, convex relaxation, image annotation",
author = "Tsai, {Min Hsuan} and Jinjun Wang and Tong Zhang and Yihong Gong and Huang, {Thomas S.}",
year = "2011",
doi = "10.1109/ICIP.2011.6116168",
language = "English (US)",
isbn = "9781457713033",
series = "Proceedings - International Conference on Image Processing, ICIP",
pages = "2497--2500",
booktitle = "ICIP 2011",
note = "2011 18th IEEE International Conference on Image Processing, ICIP 2011 ; Conference date: 11-09-2011 Through 14-09-2011",
}