RoadTones: Tone Controllable Text Generation from Road Event Videos

Problem
Existing video-language models primarily focus on generating factual descriptions of road events but lack the capability to control the tone, urgency, or style of these descriptions. This limitation is particularly critical in communication-sensitive environments where the effectiveness of a message is contingent on both its content and its presentation. The authors address this gap by introducing a novel dataset and model for tone-controllable video captioning, which is essential for applications requiring nuanced communication. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors introduce the RoadTones-51K dataset, which is generated through a human-validated pipeline that enriches existing road video corpora with diverse tonal annotations and multi-tone captions. The dataset serves as the foundation for training the proposed model, RoadTones-VL-CoT, which is a controllable video-to-text architecture. This model incorporates a Chain-of-Thought (CoT) mechanism that generates intermediate drafts conditioned on the desired tone, enhancing interpretability. The training process and specific compute resources are not disclosed, but the model’s architecture is designed to facilitate tone control during the captioning process. Additionally, the authors present RoadTones-Eval, a new evaluation suite that assesses both factual consistency and tone adherence, providing a comprehensive framework for evaluating the model’s performance.

Results
The authors conducted a user study to validate the quality of the generated captions, focusing on tone control and factual consistency. While specific numerical results are not provided in the abstract, the study indicates that the RoadTones-VL-CoT model significantly outperforms existing baselines in terms of both tone adherence and factual accuracy. The evaluation metrics from RoadTones-Eval suggest that the model achieves a higher degree of user satisfaction compared to traditional video captioning models, although exact effect sizes and benchmark comparisons are not detailed.

Limitations
The authors acknowledge that the dataset may not encompass all possible tonal variations and that the model’s performance could be limited by the diversity of the training data. Additionally, the reliance on human validation for dataset generation may introduce biases that affect generalizability. The authors do not discuss potential computational inefficiencies or scalability issues related to the Chain-of-Thought mechanism, which could impact real-time applications.

Why it matters
This work has significant implications for the development of context-sensitive AI systems capable of nuanced communication. By enabling tone control in video captioning, the RoadTones framework can enhance user engagement and comprehension in various applications, such as autonomous driving systems, emergency response communications, and educational tools. The introduction of a dedicated evaluation suite also sets a precedent for future research in tone-aware AI, encouraging the exploration of similar capabilities in other domains.

Authors: Chirag Parikh, Siddhi Pravin Lipare, Ravi Kiran Sarvadevabhatla
Source: arXiv:2605.21411
URL: https://arxiv.org/abs/2605.21411v1