Whether you can locate or not? Interactive Referring Expression
  Generation
Fulong Ye Yuxing Long Fangxiang Feng Xiaojie Wang
Referring Expression Generation (REG) aims to generate unambiguous Referring
Expressions (REs) for objects in a visual scene, with a dual task of Referring
Expression Comprehension (REC) to locate the referred object. Existing methods
construct REG models independently by using only the REs as ground truth for
model training, without considering the potential interaction between REG and
REC models. In this paper, we propose an Interactive REG (IREG) model that can
interact with a real REC model, utilizing signals indicating whether the object
is located and the visual region located by the REC model to gradually modify
REs. Our experimental results on three RE benchmark datasets, RefCOCO,
RefCOCO+, and RefCOCOg show that IREG outperforms previous state-of-the-art
methods on popular evaluation metrics. Furthermore, a human evaluation shows
that IREG generates better REs with the capability of interaction.