We introduce dense relational captioning, a novel image captioning task which
aims to generate multiple captions with respect to relational information
between objects in a visual scene. Relational captioning provides explicit
descriptions for each relationship between object combinations. This framework
is advantageous in both diversity and amount of information, leading to a
comprehensive image understanding based on relationships, e.g., relational
proposal generation. For relational understanding between objects, the
part-of-speech (POS; i.e., subject-object-predicate categories) can be a
valuable prior information to guide the causal sequence of words in a caption.
We enforce our framework to learn not only to generate captions but also to
understand the POS of each word. To this end, we propose the multi-task
triple-stream network (MTTSNet) which consists of three recurrent units
responsible for each POS which is trained by jointly predicting the correct
captions and POS for each word. In addition, we found that the performance of
MTTSNet can be improved by modulating the object embeddings with an explicit
relational module. We demonstrate that our proposed model can generate more
diverse and richer captions, via extensive experimental analysis on large scale
datasets and several metrics. Then, we present applications of our framework to
holistic image captioning, scene graph generation, and retrieval tasks.

本篇研究提出了一种新的图像描述任务：密集关系字幕，该任务旨在生成多个字幕，涉及视觉场景中对象之间的关系信息。通过使用多任务三重流网络（MTTSNet）训练识别 POS（主客观谓宾分类）并生成字幕，同时使用明确的关系模块来改进它的表现。该模型在大型数据集和多个指标上经过了广泛的实验分析，能够生成更多样化、更丰富的字幕，并将其应用于整体图像字幕，场景图生成和检索任务。