Hong, Xudong; Demberg, Vera; Sayeed, Asad; Zheng, Qiankun; Schiele, Bernt

Visual Coherence Loss for Coherent and Visually Grounded Story Generation

Rogers, Anna; Boyd-Graber, Jordan; Okazaki, Naoaki (Ed.): Findings of the Association for Computational Linguistics: ACL 2023, Association for Computational Linguistics, pp. 9456-9470, Toronto, Canada, 2023.

Local coherence is essential for long-form text generation models. We identify two important aspects of local coherence within the visual storytelling task: (1) the model needs to represent re-occurrences of characters within the image sequence in order to mention them correctly in the story; (2) character representations should enable us to find instances of the same characters and distinguish different characters. In this paper, we propose a loss function inspired by a linguistic theory of coherence for self-supervised learning for image sequence representations. We further propose combining features from an object and a face detector to construct stronger character features. To evaluate input-output relevance that current reference-based metrics don{‚}t measure, we propose a character matching metric to check whether the models generate referring expressions correctly for characters in input image sequences. Experiments on a visual story generation dataset show that our proposed features and loss function are effective for generating more coherent and visually grounded stories.