While image captioning has gained significant attention, the potential of captioning time-series images, prevalent in areas like finance and healthcare, remains largely untapped. Existing time-series captioning methods typically offer generic, domain-agnostic descriptions of time-series shapes and struggle to adapt to new domains without substantial retraining. To address these limitations, we introduce TADACap, a retrieval-based framework to generate domain-aware captions for time-series images, capable of adapting to new domains without retraining. Building on TADACap, we propose a novel retrieval strategy that retrieves diverse image-caption pairs from a target domain database, namely TADACap-diverse. We benchmarked TADACap-diverse against state-of-the-art methods and ablation variants. TADACap-diverse demonstrates comparable semantic accuracy while requiring significantly less annotation effort.
@article{arxiv.2504.11441,
title = {TADACap: Time-series Adaptive Domain-Aware Captioning},
author = {Elizabeth Fons and Rachneet Kaur and Zhen Zeng and Soham Palande and Tucker Balch and Svitlana Vyetrenko and Manuela Veloso},
journal= {arXiv preprint arXiv:2504.11441},
year = {2025}
}