This work introduces a novel task, location-aware visual question generation (LocaVQG), which aims to generate engaging questions from data relevant to a particular geographical location. Specifically, we represent such location-aware information with surrounding images and a GPS coordinate. To tackle this task, we present a dataset generation pipeline that leverages GPT-4 to produce diverse and sophisticated questions. Then, we aim to learn a lightweight model that can address the LocaVQG task and fit on an edge device, such as a mobile phone. To this end, we propose a method which can reliably generate engaging questions from location-aware information. Our proposed method outperforms baselines regarding human evaluation (e.g., engagement, grounding, coherence) and automatic evaluation metrics (e.g., BERTScore, ROUGE-2). Moreover, we conduct extensive ablation studies to justify our proposed techniques for both generating the dataset and solving the task.

翻译：本文提出了一项新任务——位置感知视觉问题生成（LocaVQG），旨在根据特定地理位置的相关数据生成引人入胜的问题。具体而言，我们通过周围图像和GPS坐标来表示这种位置感知信息。为应对此任务，我们设计了一个数据集生成流程，利用GPT-4生成多样且复杂的问题。随后，我们致力于学习一个轻量级模型，使其能够解决LocaVQG任务并适配于边缘设备（如手机）。为此，我们提出了一种方法，可从位置感知信息中可靠地生成引人入胜的问题。所提出的方法在人工评估（如参与度、基础性、连贯性）和自动评估指标（如BERTScore、ROUGE-2）上均优于基线模型。此外，我们进行了广泛的消融实验，以验证所提出的数据集生成与任务求解技术的有效性。