Reassessing Vision-in-the-Loop Search: What Visual Acquisition Adds to Interactive Exploration

Abstract

Research on vision-in-the-loop search is represented by distinct lines of work, including Query-by-visual-search: multimodal framework for content-based image retrieval, Multimodal clothing recognition for semantic search in unconstrained surveillance imagery, and Efficient Search in a Panoramic Image Database for Long-term Visual Localization. The references are mapped across visual acquisition, interactive exploration, and active visual search. Particular attention is paid to how evaluation protocols shape apparently conflicting conclusions and how those conclusions should be interpreted outside their original settings. The resulting evidence map identifies well-supported practices, unresolved tensions, and concrete priorities for comparative research. It emphasizes transparent assumptions, reproducible protocols, and evaluation measures that match the intended use.