StrAD: A Streaming Method and Benchmark for Audio Description Generation for Long-form Videos
Researchers introduce StrAD, a benchmark and streaming method for generating audio descriptions for full-length videos. It reformulates AD generation as streaming dense video captioning, inserting descriptions into existing transcripts without ground-truth timestamps. The fine-tuned StrAD-FT achieves state-of-the-art results on several benchmarks, while being the first streaming approach for full-video AD generation.
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[Submitted on 12 Aug 2026]
Title:StrAD: A Streaming Method and Benchmark for Audio Description Generation for Long-form Videos
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Abstract:Visual content is the dominant medium of communication, yet without audio descriptions (ADs), it remains inaccessible to blind and low-vision people. ADs narrate context-relevant visual events during natural audio pauses. Manually creating ADs is expensive, limiting coverage to a small fraction of available content. Most existing automatic AD generation methods frame the task as video clip captioning, requiring ground-truth timestamps and additional context cues such as character databases. Current benchmarks reinforce this framing, consisting of short video segments paired with automatic or task-mismatched annotations. We introduce StrAD, a benchmark for long-form AD generation on full-length videos spanning diverse genres such as movies, documentaries, short films, performances, and video games. We reformulate AD generation as streaming dense video captioning. Our approach processes full-length videos with a sliding window, inserting ADs into existing transcripts without ground-truth timestamps, and supports both fine-tuned models and zero-shot prompting of vision-language models. On the segment-level task with given timestamps, our fine-tuned StrAD-FT sets the state of the art on CMD-AD with 36.3 CIDEr (+10.0 over Shot-by-shot), establishes a reference point on StrAD (51.0 CIDEr), and remains competitive on MAD-Eval at 24.9 CIDEr. On the full-video streaming task, StrAD-FT reaches a SODA score of 2.4 against 1.1 for our zero-shot baseline StrAD-Zero, though both exhibit limitations in temporal localization and narrative coherence. While prior work has tackled full-video AD generation in an offline, multi-stage fashion, ours is the first streaming approach, generating ADs on the fly without ground-truth timestamps. StrAD makes progress on full-video AD generation measurable, a prerequisite for scaling accessibility.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.12549 [cs.CV]
(or arXiv:2608.12549v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.12549
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Julian Spravil [view email] [v1] Wed, 12 Aug 2026 19:39:17 UTC (1,568 KB)
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