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New method drastically cuts cost of evaluating large-scale image retrieval systems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for evaluating instance retrieval systems that significantly reduces the computational cost associated with large-scale datasets. By analyzing the relationship between gallery size and retrieval performance, they found that a small random sample of images is sufficient to accurately estimate retrieval metrics. This 'tail-aware' evaluation approach, demonstrated on datasets like ILIAS and Google Landmarks v2, requires substantially less indexing time and storage, making retrieval evaluation faster, cheaper, and more environmentally friendly. AI

IMPACT Reduces computational costs for AI researchers evaluating image retrieval models, enabling faster iteration and greener experimentation.

RANK_REASON Academic paper detailing a new methodology for retrieval evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method drastically cuts cost of evaluating large-scale image retrieval systems

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Academic paper detailing a new methodology for retrieval evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Shai Avidan ·

    Web Scale on a Budget: Tail-Aware Retrieval Evaluation

    A common practice in instance retrieval is to grow the gallery size to improve a benchmark's discrimination capability. One prominent example is ILIAS, which adds up to 100 million distractor images. The prevailing assumption is that faithful evaluation demands web scale, and tha…

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