TADACap: Time-series Adaptive Domain-Aware Captioning
This addresses the need for adaptive captioning in time-series domains, offering a practical solution for applications where generic descriptions are insufficient, though it appears incremental as it builds on retrieval-based approaches.
The paper tackles the problem of generating domain-aware captions for time-series images, which are common in fields like finance and healthcare, by introducing TADACap, a retrieval-based framework that adapts to new domains without retraining. The result shows that TADACap-diverse achieves comparable semantic accuracy with significantly less annotation effort compared to state-of-the-art methods.
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.