  Recurrent neural networks (RNN) are popular for many computer vision tasks,
including multi-label classification. Since RNNs produce sequential outputs,
labels need to be ordered for the multi-label classification task. Current
approaches sort labels according to their frequency, typically ordering them in
either rare-first or frequent-first. These imposed orderings do not take into
account that the natural order to generate the labels can change for each
image, e.g.\ first the dominant object before summing up the smaller objects in
the image. Therefore, in this paper, we propose ways to dynamically order the
ground truth labels with the predicted label sequence. This allows for the
faster training of more optimal LSTM models for multi-label classification.
Analysis evidences that our method does not suffer from duplicate generation,
something which is common for other models. Furthermore, it outperforms other
CNN-RNN models, and we show that a standard architecture of an image encoder
and language decoder trained with our proposed loss obtains the
state-of-the-art results on the challenging MS-COCO, WIDER Attribute and
PA-100K and competitive results on NUS-WIDE.
