Work

Text2Traj2Text: Learning-by-Synthesis Framework for Contextual Captioning of Human Movement Trajectories

Year: 2024

Type: conference-paper

Abstract: This paper presents Text2Traj2Text, a novel learning-by-synthesis framework for captioning possible contexts behind shopper's trajectory data in retail stores.Our work will impact various retail applications that need better customer understanding, such as targeted advertising and inventory management.The key idea is leveraging large language models to synthesize a diverse and realistic collection of contextual captions as well as the corresponding movement trajectories on a store map.Despite learned from fully synthesized data, the captioning model can generalize well to trajectories/captions created by real human subjects.Our systematic evaluation confirmed the effectiveness of the proposed framework over competitive approaches in terms of ROUGE and BERT Score metrics.

Source: International Conference on Natural Language Generation

DOI: 10.18653/v1/2024.inlg-main.24

Authors: Hikaru Asano, Ryo Yonetani, Taiki Sekii, Hiroki Ouchi

Institutions: RIKEN, RIKEN Center for Advanced Intelligence Project, The University of Tokyo, CyberAgent (Japan)

Language: English

Topic: Multimodal Machine Learning Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4411113552

FWCI:

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