UI-Lens: Assessing General MLLMs' Potential to Automate UI Display Quality Assurance
Wei Xiang, Yexinrui Wu, Xinli Chen, Xinran Li, Shi Chen
Abstract
User Interface (UI) display defect detection poses challenges far beyond UI understanding, requiring fine-grained element boundary understanding, missing-content detection, and reasoning about sequential interface semantic consistency. However, the capabilities of multimodal large language models (MLLMs) and vision-language models (VLMs) for detecting UI defects in realistic, complex interfaces have not been systematically validated. To fill this gap, we present UI-Lens, the first multi-dimensional UI display detection benchmark for Chinese-language UI scenarios. The dataset comprises 4,759 pages meticulously annotated by design experts, covering six core display defect categories. We conduct a systematic evaluation of 10 mainstream models (8 closed-source, 2 open-source). Results show clear shortcomings in current models: for tasks requiring fine-grained element boundary understanding, performance is near random, with task-average F1 scores of 20.36% and 31.21% on Text Overflow and Container Overlap, respectively; for sequential interface semantic consistency (e.g., Text Inconsistency), the task-average F1 score is only 10.61%, indicating severe underperformance. We release UI-Lens to catalyze research toward more robust UI display defect detection with fine-grained boundary awareness in realistic, complex interfaces.
BibTeX
@inproceedings{cvpr2026_uilensassessingg,
title = {UI-Lens: Assessing General MLLMs' Potential to Automate UI Display Quality Assurance},
author = {Wei Xiang and Yexinrui Wu and Xinli Chen and Xinran Li and Shi Chen},
booktitle = {CVPR 2026},
year = {2026}
}