VAST-27M Annotations: Vision-Audio-Subtitle-Text Labels for Omni-Modality Models

Annotations from the VAST-27M dataset created for the 2024 paper "VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset". The dataset was derived from work by researchers at the University of Chinese Academy of Sciences and the Institute of Automation, Chinese Academy of Science. It was uploaded to the Hugging Face platform by the user 'it-just-works' on September 10, 2024.

Use Cases

• Training vision-audio-text foundation models based on the described omni-modality annotations.
• Benchmarking multimodal model performance using the structured annotations from the VAST-27M source.
• Conducting research on cross-modal alignment and representation learning based on the subtitle and text labels.

Strengths

• Annotations are derived from a dataset created for a peer-reviewed 2024 research publication.
• Source data originates from a research institution, the University of Chinese Academy of Sciences and the Institute of Automation, Chinese Academy of Science.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Derived from the VAST-27M dataset by researchers at the University of Chinese Academy of Sciences and the Institute of Automation, Chinese Academy of Science.

Freshness

Last updated 2024-09-10 14:05:07; freshness should be verified.