2023/08/02 by Renzhi Wu, Jingfan Meng, Wu, Renzhi +7
Computer Science · #Databases (cs.DB) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Multimodal Machine Learning Applications #Music and Audio Processing #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.2308.00909
openalex publication_date 2023/08/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this vision paper, we propose a shift in perspective for improving the effectiveness of similarity search. Rather than focusing solely on enhancing the data quality, particularly machine learning-generated embeddings, we advocate for a more comprehensive approach that also enhances the underpinning search mechanisms. We highlight three novel avenues that call for a redefinition of the similarity search problem: exploiting implicit data structures and distributions, engaging users in an iterative feedback loop, and moving beyond a single query vector. These novel pathways have gained relevance in emerging applications such as large-scale language models, video clip retrieval, and data labeling. We discuss the corresponding research challenges posed by these new problem areas and share insights from our preliminary discoveries.