Cross‑Modal Video Open‑Tag Mining: Techniques, Methods, and Applications

This article introduces the technology of cross‑modal video open‑tag mining, outlining its background, the challenges of open‑ended label extraction, and the importance of multi‑dimensional video understanding.

It reviews related research methods, including precise label classification using hierarchical taxonomies, traditional 3D‑CNN, RNN/LSTM, TSN, NeXtVLAD, and multimodal Transformers, and discusses the limitations of each.

The core of the open‑tag solution from 360 AI Research Institute is presented, covering the four‑stage architecture (data sources, tag mining, tag relevance, ranking), keyword extraction, multi‑label classification, tag graph construction, and fusion‑optimization techniques.

Further sections describe the tag discrimination model, the video‑content relevance model, few‑shot learning with multimodal prompts, and the large‑scale Zero dataset for cross‑modal pre‑training.

Finally, the article explores application prospects such as unsupervised tag coverage improvement, semantic vector retrieval, content moderation, cold‑start tagging, and offline tag‑library construction, and includes a Q&A session.