  Automatically describing video content with text description is challenging
but important task, which has been attracting a lot of attention in computer
vision community. Previous works mainly strive for the accuracy of the
generated sentences, while ignoring the sentences diversity, which is
inconsistent with human behavior. In this paper, we aim to caption each video
with multiple descriptions and propose a novel framework. Concretely, for a
given video, the intermediate latent variables of conventional encode-decode
process are utilized as input to the conditional generative adversarial network
(CGAN) with the purpose of generating diverse sentences. We adopt different
Convolutional Neural Networks (CNNs) as our generator that produces
descriptions conditioned on latent variables and discriminator that assesses
the quality of generated sentences. Simultaneously, a novel DCE metric is
designed to assess the diverse captions. We evaluate our method on the
benchmark datasets, where it demonstrates its ability to generate diverse
descriptions and achieves superior results against other state-of-the-art
methods.
