Large-scale video repositories are increasingly available for modern video understanding and generation tasks. However, transforming raw videos into high-quality, task-specific datasets remains costly and inefficient. We present DataCube, an intelligent platform for automatic video processing, multi-dimensional profiling, and query-driven retrieval. DataCube constructs structured semantic representations of video clips and supports hybrid retrieval with neural re-ranking and deep semantic matching. Through an interactive web interface, users can efficiently construct customized video subsets from massive repositories for training, analysis, and evaluation, and build searchable systems over their own private video collections. The system is publicly accessible at https://datacube.baai.ac.cn/. Demo Video: https://baai-data-cube.ks3-cn-beijing.ksyuncs.com/custom/Adobe%20Express%20-%202%E6%9C%8818%E6%97%A5%20%281%29%281%29%20%281%29.mp4
@article{arxiv.2602.16231,
title = {DataCube: A Video Retrieval Platform via Natural Language Semantic Profiling},
author = {Yiming Ju and Hanyu Zhao and Quanyue Ma and Donglin Hao and Chengwei Wu and Ming Li and Songjing Wang and Tengfei Pan},
journal= {arXiv preprint arXiv:2602.16231},
year = {2026}
}
Comments
This paper is under review for the IJCAI-ECAI 2026 Demonstrations Track