  The number of social images has exploded by the wide adoption of social
networks, and people like to share their comments about them. These comments
can be a description of the image, or some objects, attributes, scenes in it,
which are normally used as the user-provided tags. However, it is well-known
that user-provided tags are incomplete and imprecise to some extent. Directly
using them can damage the performance of related applications, such as the
image annotation and retrieval. In this paper, we propose to learn an image
annotation model and refine the user-provided tags simultaneously in a
weakly-supervised manner. The deep neural network is utilized as the image
feature learning and backbone annotation model, while visual consistency,
semantic dependency, and user-error sparsity are introduced as the constraints
at the batch level to alleviate the tag noise. Therefore, our model is highly
flexible and stable to handle large-scale image sets. Experimental results on
two benchmark datasets indicate that our proposed model achieves the best
performance compared to the state-of-the-art methods.
