  Most RNN-based image captioning models receive supervision on the output
words to mimic human captions. Therefore, the hidden states can only receive
noisy gradient signals via layers of back-propagation through time, leading to
less accurate generated captions. Consequently, we propose a novel framework,
Hidden State Guidance (HSG), that matches the hidden states in the caption
decoder to those in a teacher decoder trained on an easier task of autoencoding
the captions conditioned on the image. During training with the REINFORCE
algorithm, the conventional rewards are sentence-based evaluation metrics
equally distributed to each generated word, no matter their relevance. HSG
provides a word-level reward that helps the model learn better hidden
representations. Experimental results demonstrate that HSG clearly outperforms
various state-of-the-art caption decoders using either raw images or detected
objects as inputs.
