Video-guided Machine Translation with Global Video Context
Video-guided Multimodal Translation (VMT) has advanced significantly in recent years. However, most existing methods rely on locally aligned video segments paired one-to-one with subtitles, limiting their ability to capture global narrative context across multiple segments in long videos. To overcome this limitation, we propose a globally video-guided multimodal translation framework that leverages a pretrained semantic encoder and vector database-based subtitle retrieval to construct a context set of video segments closely related to the target subtitle semantics. An attention mechanism is employed to focus on highly relevant visual content, while preserving the remaining video features ...
| Authors | Jian Chen, JinZe Lv, Zi Long et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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