Vector databases have recently gained significant attention due to the emergence of large language models that produce vector embeddings for text. Existing vector databases can be broadly categorized into two types: specialized and generalized. Specialized vector databases are explicitly designed and optimized for managing vector data, while generalized ones support vector data management within a general purpose database. While specialized vector databases are interesting, there is a substantial customer base interested in generalized vector databases for various reasons, e.g., a reluctance to move data out of relational databases to reduce data silos and costs, the desire to use SQL, and the need for more sophisticated query processing of vector and non-vector data. However, generalized vector databases face two main challenges: performance and interoperability of vector search with SQL, such as combining vector search with filters, joins, or even fulltext search. In this paper, we present SingleStore-V, a full-fledged generalized vector database integrated into SingleStore, a modern distributed relational database optimized for both OLAP and OLTP workloads. SingleStore-V achieves high performance and interoperability via a suite of optimizations. Experiments on standard vector benchmarks show that SingleStore-V performs comparably to Milvus, a highly-optimized specialized vector database, and significantly outperforms pgvector, a popular generalized vector database in PostgreSQL. We believe this paper will shed light on integrating vector search into relational databases in general, as many design concepts and optimizations apply to other databases.

Human parsing and pose estimation, regarded as two fundamental tasks to analyze human in the wild, are the basis of upper-level tasks, such as human action recognition and person re-identification. The lack of a comprehensive multi-person dataset, which contains the annotations of both human part labels and skeleton keypoint labels, makes that most of the joint learning work for pose estimation and human parsing can only focus on the single-person scene. To fill this gap, we proposed a comprehensive multi-person dataset Look into Multi-Person (LIMP) with 10,082 multi-person images. And we adopt data augmentation strategies to enrich the dataset's diversity which contains more texture, more occlusion, and more in-the-wild images than CIHP. To the best of our knowledge, this is the first comprehensive multi-person dataset for pose estimation and human parsing.