Image description generation plays an important role in many real-world
applications, such as image retrieval, automatic navigation, and disabled
people support. A well-developed task of image description generation is image
captioning, which usually generates a short captioning sentence and thus
neglects many of fine-grained properties, e.g., the information of subtle
objects and their relationships. In this paper, we study the visual paragraph
generation, which can describe the image with a long paragraph containing rich
details. Previous research often generates the paragraph via a hierarchical
Recurrent Neural Network (RNN)-like model, which has complex memorising,
forgetting and coupling mechanism. Instead, we propose a novel pure CNN model,
ParaCNN, to generate visual paragraph using hierarchical CNN architecture with
contextual information between sentences within one paragraph. The ParaCNN can
generate an arbitrary length of a paragraph, which is more applicable in many
real-world applications. Furthermore, to enable the ParaCNN to model paragraph
comprehensively, we also propose an adversarial twin net training scheme.
During training, we force the forwarding network's hidden features to be close
to that of the backwards network by using adversarial training. During testing,
we only use the forwarding network, which already includes the knowledge of the
backwards network, to generate a paragraph. We conduct extensive experiments on
the Stanford Visual Paragraph dataset and achieve state-of-the-art performance.

本文提出了一种使用纯 CNN 模型生成可包含丰富细节的长段落视觉描述的方法，使用上下文信息的层次 CNN 架构，以及对抗性训练方案，以实现全面建模段落的目的，并在 Stanford 视觉段落数据集上获得了最先进的性能。