DataCube: A Video Retrieval Platform via Natural Language Semantic Profiling
This addresses the challenge for researchers and practitioners in video understanding and generation who need to build high-quality datasets from large-scale video repositories, though it appears incremental as it builds on existing retrieval and profiling techniques.
The paper tackles the problem of inefficient and costly transformation of raw videos into task-specific datasets by presenting DataCube, an intelligent platform for automatic video processing and query-driven retrieval, which enables users to efficiently construct customized video subsets from massive repositories.
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