High-Precision Cross-Modal Alignment Datasets

Our cross-modal alignment datasets focus on precise matching and time-semantic calibration between image-text, video-text, audio-text and multiple modalities. Adopt manual fine calibration and semantic annotation, unify timeline, content logic and scene correlation of different modalities. Covering daily life, games, advertising, education and other scenarios, samples include short matching and long-sequence cross-modal content.

We eliminate time offset and semantic mismatch through multi-round inspection, and form standardized alignment specifications suitable for model training. Support multilingual cross-modal matching and industry exclusive customized alignment data production.

As core training data for multimodal models, it is widely used in image captioning, video subtitling, audio-text matching and generative creation tasks, effectively solving the problem of cross-modal semantic dislocation, improving model alignment accuracy and reasoning stability.