RoadTones: Tone Controllable Text Generation from Road Event Videos
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918514701369344 |
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| author | Parikh, Chirag Lipare, Siddhi Pravin Sarvadevabhatla, Ravi Kiran |
| author_facet | Parikh, Chirag Lipare, Siddhi Pravin Sarvadevabhatla, Ravi Kiran |
| contents | Existing video-language models can generate factual descriptions of road events but lack control over how these events are expressed: their tone, urgency, or style. This limits deployment in communication-critical settings where the effectiveness of a message depends on both content and presentation, not just factual accuracy. To mitigate this, we introduce a comprehensive dataset-model-evaluation suite for tone-controllable road video captioning. Our human-validated data generation pipeline expands road-video corpora with diverse tonal annotations and multi-tone captions, yielding the RoadTones-51K dataset. We propose RoadTones-VL-CoT, a controllable video-to-text model that also generates tone-conditioned Chain-of-Thought intermediate drafts for interpretability. We also introduce RoadTones-Eval, a new evaluation suite that jointly measures factual consistency and tone adherence. In addition, we conducted a user study whose results validate caption quality, tone control, and factual consistency. Together, these contributions lay the foundation for context-sensitive tone-controllable video captioning. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_21411 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RoadTones: Tone Controllable Text Generation from Road Event Videos Parikh, Chirag Lipare, Siddhi Pravin Sarvadevabhatla, Ravi Kiran Computer Vision and Pattern Recognition Existing video-language models can generate factual descriptions of road events but lack control over how these events are expressed: their tone, urgency, or style. This limits deployment in communication-critical settings where the effectiveness of a message depends on both content and presentation, not just factual accuracy. To mitigate this, we introduce a comprehensive dataset-model-evaluation suite for tone-controllable road video captioning. Our human-validated data generation pipeline expands road-video corpora with diverse tonal annotations and multi-tone captions, yielding the RoadTones-51K dataset. We propose RoadTones-VL-CoT, a controllable video-to-text model that also generates tone-conditioned Chain-of-Thought intermediate drafts for interpretability. We also introduce RoadTones-Eval, a new evaluation suite that jointly measures factual consistency and tone adherence. In addition, we conducted a user study whose results validate caption quality, tone control, and factual consistency. Together, these contributions lay the foundation for context-sensitive tone-controllable video captioning. |
| title | RoadTones: Tone Controllable Text Generation from Road Event Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.21411 |