  In this paper, we propose the joint learning attention and recurrent neural
network (RNN) models for multi-label classification. While approaches based on
the use of either model exist (e.g., for the task of image captioning),
training such existing network architectures typically require pre-defined
label sequences. For multi-label classification, it would be desirable to have
a robust inference process, so that the prediction error would not propagate
and thus affect the performance. Our proposed model uniquely integrates
attention and Long Short Term Memory (LSTM) models, which not only addresses
the above problem but also allows one to identify visual objects of interests
with varying sizes without the prior knowledge of particular label ordering.
More importantly, label co-occurrence information can be jointly exploited by
our LSTM model. Finally, by advancing the technique of beam search, prediction
of multiple labels can be efficiently achieved by our proposed network model.
