Instance-Level Generation for Representation Learning

• URL: http://arxiv.org/abs/2510.09171v1
• Date: Fri, 10 Oct 2025 09:14:33 GMT
• Title: Instance-Level Generation for Representation Learning
• Authors: Yankun Wu, Zakaria Laskar, Giorgos Kordopatis-Zilos, Noa Garcia, Giorgos Tolias,
• Abstract summary: Instance-level recognition (ILR) focuses on identifying individual objects rather than broad categories.<n>We introduce a novel approach that synthetically generates diverse object instances from multiple domains.<n>Our method is the first to address ILR-specific challenges without relying on any real images.
• Score: 20.97048848139392
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Instance-level recognition (ILR) focuses on identifying individual objects rather than broad categories, offering the highest granularity in image classification. However, this fine-grained nature makes creating large-scale annotated datasets challenging, limiting ILR's real-world applicability across domains. To overcome this, we introduce a novel approach that synthetically generates diverse object instances from multiple domains under varied conditions and backgrounds, forming a large-scale training set. Unlike prior work on automatic data synthesis, our method is the first to address ILR-specific challenges without relying on any real images. Fine-tuning foundation vision models on the generated data significantly improves retrieval performance across seven ILR benchmarks spanning multiple domains. Our approach offers a new, efficient, and effective alternative to extensive data collection and curation, introducing a new ILR paradigm where the only input is the names of the target domains, unlocking a wide range of real-world applications.

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