Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hidden State Guidance: Improving Image Captioning using An Image
  Conditioned Autoencoder   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.

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The categories of this paper are: nlin
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The categories of this paper are: nucl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: eess
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The categories of this paper are: econ
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: stat
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The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: atom-ph