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Most RNN-based image captioning models receive supervision on the outputwords to mimic human captions. Therefore, the hidden states can only receivenoisy gradient signals via layers of back-propagation through time, leading toless accurate generated captions. Consequently, we propose a novel framework,Hidden State Guidance (HSG), that matches the hidden states in the captiondecoder to those in a teacher decoder trained on an easier task of autoencodingthe captions conditioned on the image. During training with the REINFORCEalgorithm, the conventional rewards are sentence-based evaluation metricsequally distributed to each generated word, no matter their relevance. HSGprovides a word-level reward that helps the model learn better hiddenrepresentations. Experimental results demonstrate that HSG clearly outperformsvarious state-of-the-art caption decoders using either raw images or detectedobjects as inputs.

Hidden State Guidance: Improving Image Captioning using An Image Conditioned Autoencoder