Where to put the image in an image caption generator

When a neural language model is used for caption generation, the image information can be fed to the neural network either by directly in- corporating it in a recurrent neural network { conditioning the language model by injecting image features { or in a layer following the recurrent neural network { conditioning the language model by merging the image features. While merging implies that visual features are bound at the end of the caption generation process, injecting can bind the visual features at a variety stages. In this paper we empirically show that late binding is superior to early binding in terms of di erent evaluation metrics. This suggests that the di erent modalities (visual and linguistic) for caption generation should not be jointly encoded by the RNN; rather, the multi- modal integration should be delayed to a subsequent stage. Furthermore, this suggests that recurrent neural networks should not be viewed as actu- ally generating text, but only as encoding it for prediction in a subsequent layer.peer-reviewe

What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?

In neural image captioning systems, a recurrent neural network (RNN) is typically viewed as the primary `generation' component. This view suggests that the image features should be `injected' into the RNN. This is in fact the dominant view in the literature. Alternatively, the RNN can instead be viewed as only encoding the previously generated words. This view suggests that the RNN should only be used to encode linguistic features and that only the final representation should be `merged' with the image features at a later stage. This paper compares these two architectures. We find that, in general, late merging outperforms injection, suggesting that RNNs are better viewed as encoders, rather than generators.Comment: Appears in: Proceedings of the 10th International Conference on Natural Language Generation (INLG'17