Revisiting Relevance Feedback for CLIP-based Interactive Image Retrieval

arXiv:2404.16398v1 Announce Type: new
Abstract: Many image retrieval studies use metric learning to train an image encoder. However, metric learning cannot handle differences in users' preferences, and requires data to train an image encoder. To overcome these limitations, we revisit relevance feedback, a classic technique for interactive retrieval systems, and propose an interactive CLIP-based image retrieval system with relevance feedback. Our retrieval system first executes the retrieval, collects each user's unique preferences through binary feedback, and returns images the user …

abstract arxiv clip cs.cv data differences encoder feedback however image interactive limitations retrieval studies systems train type