Muhammet Furkan Ilaslan — accepted AI-conference papers

2025

VG-TVP: Multimodal Procedural Planning via Visually Grounded Text-Video Prompting

Muhammet Furkan Ilaslan, Ali Köksal, Kevin Qinghong Lin, Burak Satar, Mike Zheng Shou, Qianli Xu

AAAI 2025technical

Large Language Model (LLM)-based agents have shown promise in procedural tasks, but the potential of multimodal instructions augmented by texts and videos to assist users remains under-explored. To address this gap, we propose the Visually Grounded Text-Video Prompting (VG-TVP) method which is a nov…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2023

GazeVQA: A Video Question Answering Dataset for Multiview Eye-Gaze Task-Oriented Collaborations

Muhammet Furkan ILASLAN, Chenan Song, Joya Chen, Difei Gao, Weixian Lei, Qianli Xu +2

EMNLP 2023long main

The usage of exocentric and egocentric videos in Video Question Answering (VQA) is a new endeavor in human-robot interaction and collaboration studies. Particularly for egocentric videos, one may leverage eye-gaze information to understand human intentions during the task. In this paper, we build a…

Cited by 0 Source ↗Scholar ↗