Location-aware Visual Question Generation With Lightweight Models

Suwono Nicholas Collin, Chen Justin Chih-yao, Hung Tun Min, Huang Ting-hao Kenneth, Liao I-bin, Li Yung-hui, Ku Lun-wei, Sun Shao-hua. Arxiv 2023

[Paper]    
BERT GPT Model Architecture RAG

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.

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