Vision Transformer Based Model for Describing a Set of Images as a Story

TL;DR

This paper introduces a novel Vision Transformer-based model for visual storytelling that effectively captures visual variation and context from image sets, resulting in more coherent and relevant stories.

Contribution

It combines Vision Transformers with sequence encoding and attention mechanisms to improve visual storytelling from image sets, outperforming existing models.

Findings

Outperforms current state-of-the-art models on VIST dataset

Effectively captures visual variation and context in image sets

Enhances story coherence and relevance

Abstract

Visual Story-Telling is the process of forming a multi-sentence story from a set of images. Appropriately including visual variation and contextual information captured inside the input images is one of the most challenging aspects of visual storytelling. Consequently, stories developed from a set of images often lack cohesiveness, relevance, and semantic relationship. In this paper, we propose a novel Vision Transformer Based Model for describing a set of images as a story. The proposed method extracts the distinct features of the input images using a Vision Transformer (ViT). Firstly, input images are divided into 16X16 patches and bundled into a linear projection of flattened patches. The transformation from a single image to multiple image patches captures the visual variety of the input visual patterns. These features are used as input to a Bidirectional-LSTM which is part of the sequence encoder. This captures the past and future image context of all image patches. Then, an attention mechanism is implemented and used to increase the discriminatory capacity of the data fed into the language model, i.e. a Mogrifier-LSTM. The performance of our proposed model is evaluated using the Visual Story-Telling dataset (VIST), and the results show that our model outperforms the current state of the art models.