Retrieval-enriched zero-shot image classification in low-resource domains

• URL: http://arxiv.org/abs/2411.00988v1
• Date: Fri, 01 Nov 2024 19:24:55 GMT
• Title: Retrieval-enriched zero-shot image classification in low-resource domains
• Authors: Nicola Dall'Asen, Yiming Wang, Enrico Fini, Elisa Ricci,
• Abstract summary: Low-resource domains present significant challenges for language and visual understanding tasks. Recent advancements in Vision-Language Models (VLM) have shown promising results in high-resource domains but fall short in low-resource concepts that are under-represented. We tackle the challenging task of zero-shot low-resource image classification from a novel perspective.
• Score: 23.529317590033845
• License:
• Abstract: Low-resource domains, characterized by scarce data and annotations, present significant challenges for language and visual understanding tasks, with the latter much under-explored in the literature. Recent advancements in Vision-Language Models (VLM) have shown promising results in high-resource domains but fall short in low-resource concepts that are under-represented (e.g. only a handful of images per category) in the pre-training set. We tackle the challenging task of zero-shot low-resource image classification from a novel perspective. By leveraging a retrieval-based strategy, we achieve this in a training-free fashion. Specifically, our method, named CoRE (Combination of Retrieval Enrichment), enriches the representation of both query images and class prototypes by retrieving relevant textual information from large web-crawled databases. This retrieval-based enrichment significantly boosts classification performance by incorporating the broader contextual information relevant to the specific class. We validate our method on a newly established benchmark covering diverse low-resource domains, including medical imaging, rare plants, and circuits. Our experiments demonstrate that CORE outperforms existing state-of-the-art methods that rely on synthetic data generation and model fine-tuning.

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