Sunqi Fan — accepted AI-conference papers

2025

Tool-Augmented Spatiotemporal Reasoning for Streamlining Video Question Answering Task

Sunqi Fan, Jiashuo Cui, Meng-Hao Guo, Shuojin Yang

NeurIPS 2025poster

Video Question Answering (VideoQA) task serves as a critical playground for evaluating whether foundation models can effectively perceive, understand, and reason about dynamic real-world scenarios. However, existing Multimodal Large Language Models (MLLMs) struggle with simultaneously ensuring the a…

Cited by 9 Source ↗code ↗Scholar ↗

2024

FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering

Zhenyu Li, Sunqi Fan, Yu Gu, Xiuxing Li, Zhichao Duan, Bowen Dong +2

AAAI 2024technical

Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language questions posed by users. Unfortunately, the performance of most KBQA models tends to decline significantly in real-world scenar…

Cited by 87 Source ↗PDF ↗code ↗Scholar ↗