As a cross-modal task, visual storytelling aims to generate a story for an ordered image sequence automatically. Different from the image captioning task, visual storytelling requires not only modeling the relationships between objects in the image but also mining the connections between adjacent images. Recent approaches primarily utilize either end-to-end frameworks or multi-stage frameworks to generate relevant stories, but they usually overlook latent topic information. In this paper, in order to generate a more coherent and relevant story, we propose a novel method, Topic Aware Reinforcement Network for VIsual StoryTelling (TARN-VIST). In particular, we pre-extracted the topic information of stories from both visual and linguistic perspectives. Then we apply two topic-consistent reinforcement learning rewards to identify the discrepancy between the generated story and the human-labeled story so as to refine the whole generation process. Extensive experimental results on the VIST dataset and human evaluation demonstrate that our proposed model outperforms most of the competitive models across multiple evaluation metrics.

As a conversational intelligence task, visual dialog entails answering a series of questions grounded in an image, using the dialog history as context. To generate correct answers, the comprehension of the semantic dependencies among implicit visual and textual contents is critical. Prior works usually ignored the underlying relation and failed to infer it reasonably. In this paper, we propose a Visual-Textual Alignment for Graph Inference (VTAGI) network. Compared with other approaches, it makes up the lack of structural inference in visual dialog. The whole system consists of two modules, Visual and Textual Alignment (VTA) and Visual Graph Attended by Text (VGAT). Specially, the VTA module aims at representing an image with a set of integrated visual regions and corresponding textual concepts, reflecting certain semantics. The VGAT module views the visual features with semantic information as observed nodes and each node learns the relationship with others in visual graph. We also qualitatively and quantitatively evaluate the model on VisDial v1.0 dataset, showing our VTAGI outperforms previous state-of-the-art models.