  Video captioning has been a challenging and significant task that describes
the content of a video clip in a single sentence. The model of video captioning
is usually an encoder-decoder. We find that the normalization of extracted
video features can improve the final performance of video captioning.
Encoder-decoder model is usually trained using teacher-enforced strategies to
make the prediction probability of each word close to a 0-1 distribution and
ignore other words. In this paper, we present a novel architecture which
introduces a guidance module to encourage the encoder-decoder model to generate
words related to the past and future words in a caption. Based on the
normalization and guidance module, guidance module net (GMNet) is built.
Experimental results on commonly used dataset MSVD show that proposed GMNet can
improve the performance of the encoder-decoder model on video captioning tasks.
