NowYouSee Me: Context-Aware Automatic Audio Description
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arXiv
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| Format: | Preprint |
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2024
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| author | Lee, Seon-Ho Wang, Jue Fan, David Zhang, Zhikang Liu, Linda Hao, Xiang Bhat, Vimal Li, Xinyu |
| author_facet | Lee, Seon-Ho Wang, Jue Fan, David Zhang, Zhikang Liu, Linda Hao, Xiang Bhat, Vimal Li, Xinyu |
| contents | Audio Description (AD) plays a pivotal role as an application system aimed at guaranteeing accessibility in multimedia content, which provides additional narrations at suitable intervals to describe visual elements, catering specifically to the needs of visually impaired audiences. In this paper, we introduce $\mathrm{CA^3D}$, the pioneering unified Context-Aware Automatic Audio Description system that provides AD event scripts with precise locations in the long cinematic content. Specifically, $\mathrm{CA^3D}$ system consists of: 1) a Temporal Feature Enhancement Module to efficiently capture longer term dependencies, 2) an anchor-based AD event detector with feature suppression module that localizes the AD events and extracts discriminative feature for AD generation, and 3) a self-refinement module that leverages the generated output to tweak AD event boundaries from coarse to fine. Unlike conventional methods which rely on metadata and ground truth AD timestamp for AD detection and generation tasks, the proposed $\mathrm{CA^3D}$ is the first end-to-end trainable system that only uses visual cue. Extensive experiments demonstrate that the proposed $\mathrm{CA^3D}$ improves existing architectures for both AD event detection and script generation metrics, establishing the new state-of-the-art performances in the AD automation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10002 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NowYouSee Me: Context-Aware Automatic Audio Description Lee, Seon-Ho Wang, Jue Fan, David Zhang, Zhikang Liu, Linda Hao, Xiang Bhat, Vimal Li, Xinyu Computer Vision and Pattern Recognition Audio Description (AD) plays a pivotal role as an application system aimed at guaranteeing accessibility in multimedia content, which provides additional narrations at suitable intervals to describe visual elements, catering specifically to the needs of visually impaired audiences. In this paper, we introduce $\mathrm{CA^3D}$, the pioneering unified Context-Aware Automatic Audio Description system that provides AD event scripts with precise locations in the long cinematic content. Specifically, $\mathrm{CA^3D}$ system consists of: 1) a Temporal Feature Enhancement Module to efficiently capture longer term dependencies, 2) an anchor-based AD event detector with feature suppression module that localizes the AD events and extracts discriminative feature for AD generation, and 3) a self-refinement module that leverages the generated output to tweak AD event boundaries from coarse to fine. Unlike conventional methods which rely on metadata and ground truth AD timestamp for AD detection and generation tasks, the proposed $\mathrm{CA^3D}$ is the first end-to-end trainable system that only uses visual cue. Extensive experiments demonstrate that the proposed $\mathrm{CA^3D}$ improves existing architectures for both AD event detection and script generation metrics, establishing the new state-of-the-art performances in the AD automation. |
| title | NowYouSee Me: Context-Aware Automatic Audio Description |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.10002 |