  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.
