2023 年 62 巻 6 号 p. 588-598
In this review, we introduce a novel image captioning task, called Anticipation Captioning, which generates a caption for an unseen image given a sparsely temporally-ordered set of images. Our task emulates the human capacity to reason about the future based on a sparse collection of visual cues acquired over time. To address this novel challenge, we introduce a model, namely A-CAP, that predicts the caption by incorporating commonsense knowledge into a pre-trained vision-language model. Our method outperforms image captioning methods and provides a solid baseline for anticipation captioning task, as shown in both qualitative and quantitative evaluations on a customized visual storytelling dataset. We also discuss the potential applications, challenges, and future directions of this novel task.
