  While deep convolutional neural networks (CNNs) have shown a great success in
single-label image classification, it is important to note that real world
images generally contain multiple labels, which could correspond to different
objects, scenes, actions and attributes in an image. Traditional approaches to
multi-label image classification learn independent classifiers for each
category and employ ranking or thresholding on the classification results.
These techniques, although working well, fail to explicitly exploit the label
dependencies in an image. In this paper, we utilize recurrent neural networks
(RNNs) to address this problem. Combined with CNNs, the proposed CNN-RNN
framework learns a joint image-label embedding to characterize the semantic
label dependency as well as the image-label relevance, and it can be trained
end-to-end from scratch to integrate both information in a unified framework.
Experimental results on public benchmark datasets demonstrate that the proposed
architecture achieves better performance than the state-of-the-art multi-label
classification model
