Deep Convolutional Ranking for Multilabel Image Annotation
Authors: Sergey Ioffe, Alexander Toshev, Yangqing Jia, Thomas Leung, Yunchao Gong
ICLR 2014

Abstract: Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications. While existing work usually use conventional visual features for multilabel annotation, the recent deep convolutional feature shows potentials to significantly boost performance. In this work, we propose to leverage the advantage of such features and analyze key components that lead to better performances. Specifically, we show that a significant performance gain could be obtained by combining convolutional architectures with an approximate top-$k$ ranking objective function, as such objectives naturally fit the multilabel tagging problem. Our experiments on the publicly available NUS-WIDE dataset outperforms the conventional visual features by about $10%$, obtaining the best reported performance in the literature.


Peer review:
[Review]:
* A brief summary of the paper's contributions, in the context of prior work.
Paper considers several loss functions for multiclass label annotation.

* An assessment of novelty and quality. 
They have done good job, and ran experiments on the proper, large scale, however work is not very novel (or creative).

* A list of pros and cons (reasons to accept/reject).
pros:
- Gives reasonable advice, which loss function use for multi class image annotation.

cons: