Evaluating Attribute Confusion in Fashion Text-to-Image Generation

• URL: http://arxiv.org/abs/2507.07079v1
• Date: Wed, 09 Jul 2025 17:38:40 GMT
• Title: Evaluating Attribute Confusion in Fashion Text-to-Image Generation
• Authors: Ziyue Liu, Federico Girella, Yiming Wang, Davide Talon,
• Abstract summary: We build on a Visual Question Answering (VQA) localization strategy to assess entity-attribute semantics.<n>We introduce a novel automatic metric, Localized VQAScore (L-VQAScore), that combines visual localization with VQA probing both correct (reflection) and miss-localized (leakage) attribute generation.<n>On a newly curated dataset featuring challenging compositional alignment scenarios, L-VQAScore outperforms state-of-the-art T2I evaluation methods in terms of correlation with human judgments.
• Score: 7.376363744616336
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite the rapid advances in Text-to-Image (T2I) generation models, their evaluation remains challenging in domains like fashion, involving complex compositional generation. Recent automated T2I evaluation methods leverage pre-trained vision-language models to measure cross-modal alignment. However, our preliminary study reveals that they are still limited in assessing rich entity-attribute semantics, facing challenges in attribute confusion, i.e., when attributes are correctly depicted but associated to the wrong entities. To address this, we build on a Visual Question Answering (VQA) localization strategy targeting one single entity at a time across both visual and textual modalities. We propose a localized human evaluation protocol and introduce a novel automatic metric, Localized VQAScore (L-VQAScore), that combines visual localization with VQA probing both correct (reflection) and miss-localized (leakage) attribute generation. On a newly curated dataset featuring challenging compositional alignment scenarios, L-VQAScore outperforms state-of-the-art T2I evaluation methods in terms of correlation with human judgments, demonstrating its strength in capturing fine-grained entity-attribute associations. We believe L-VQAScore can be a reliable and scalable alternative to subjective evaluations.

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