ILIAS: Instance-Level Image retrieval At Scale

TL;DR

ILIAS introduces a large-scale, diverse dataset for evaluating instance-level image retrieval, highlighting the strengths and limitations of current models across various domains and conditions.

Contribution

The paper presents ILIAS, a new challenging dataset for instance-level image retrieval at scale, and provides extensive benchmarking of existing models on this dataset.

Findings

Domain-specific models excel within their domain but fail on ILIAS.

Multi-domain supervised linear adaptation improves retrieval performance.

Local descriptors remain crucial for effective re-ranking in cluttered scenes.

Abstract

This work introduces ILIAS, a new test dataset for Instance-Level Image retrieval At Scale. It is designed to evaluate the ability of current and future foundation models and retrieval techniques to recognize particular objects. The key benefits over existing datasets include large scale, domain diversity, accurate ground truth, and a performance that is far from saturated. ILIAS includes query and positive images for 1,000 object instances, manually collected to capture challenging conditions and diverse domains. Large-scale retrieval is conducted against 100 million distractor images from YFCC100M. To avoid false negatives without extra annotation effort, we include only query objects confirmed to have emerged after 2014, i.e. the compilation date of YFCC100M. An extensive benchmarking is performed with the following observations: i) models fine-tuned on specific domains, such as landmarks or products, excel in that domain but fail on ILIAS ii) learning a linear adaptation layer using multi-domain class supervision results in performance improvements, especially for vision-language models iii) local descriptors in retrieval re-ranking are still a key ingredient, especially in the presence of severe background clutter iv) the text-to-image performance of the vision-language foundation models is surprisingly close to the corresponding image-to-image case. website: https://vrg.fel.cvut.cz/ilias/