VISTA-Bench: Do Vision-Language Models Really Understand Visualized Text as Well as Pure Text?
Vision-Language Models (VLMs) have achieved impressive performance in cross-modal understanding across textual and visual inputs, yet existing benchmarks predominantly focus on pure-text queries. In real-world scenarios, language also frequently appears as visualized text embedded in images, raising the question of whether current VLMs handle such input requests comparably. We introduce VISTA-Bench, a systematic benchmark from multimodal perception, reasoning, to unimodal understanding domains. It evaluates visualized text understanding by contrasting pure-text and visualized-text questions under controlled rendering conditions. Extensive evaluation of over 20 representative VLMs reveals a pronounced modality gap: models that perform well on pure-text queries often degrade substantially when equivalent semantic content is presented as visualized text. This gap is further amplified by increased perceptual difficulty, highlighting sensitivity to rendering variations despite unchanged semantics. Overall, VISTA-Bench provides a principled evaluation framework to diagnose this limitation and to guide progress toward more unified language representations across tokenized text and pixels. The source dataset is available at https://github.com/QingAnLiu/VISTA-Bench.
698ad2d11b2dc6b37d61b23b
Qing'an Liu
claimed_verified
2026-02-11T11:19:14.834Z
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liuqingan
liuqa
user
liuqa
698ad2d11b2dc6b37d61b23c
Juntong Feng
698ad2d11b2dc6b37d61b23d
Yuhao Wang
698ad2d11b2dc6b37d61b23e
Xinzhe Han
698ad2d11b2dc6b37d61b23f
Yujie Cheng
698ad2d11b2dc6b37d61b240
Yue Zhu
698ad2d11b2dc6b37d61b241
Haiwen Diao
698ad2d11b2dc6b37d61b242
Yunzhi Zhuge
698ad2d11b2dc6b37d61b243
Huchuan Lu
VISTA-Bench: Do Vision-Language Models Really Understand Visualized Text as Well as Pure Text?
Vision-Language Models (VLMs) have achieved impressive performance in cross-modal understanding across textual and visual inputs, yet existing benchmarks predominantly focus on pure-text queries. In real-world scenarios, language also frequently appears as visualized text embedded in images, raising the question of whether current VLMs handle such input requests comparably. We introduce VISTA-Bench, a systematic benchmark from multimodal perception, reasoning, to unimodal understanding domains. It evaluates visualized text understanding by contrasting pure-text and visualized-text questions under controlled rendering conditions. Extensive evaluation of over 20 representative VLMs reveals a pronounced modality gap: models that perform well on pure-text queries often degrade substantially when equivalent semantic content is presented as visualized text. This gap is further amplified by increased perceptual difficulty, highlighting sensitivity to rendering variations despite unchanged semantics. Overall, VISTA-Bench provides a principled evaluation framework to diagnose this limitation and to guide progress toward more unified language representations across tokenized text and pixels. The source dataset is available at https://github.com/QingAnLiu/VISTA-Bench.
VISTA-Bench evaluates vision-language models' ability to understand visualized text versus pure-text queries, revealing significant performance gaps and sensitivity to rendering variations.
Vision-Language Models multimodal perception visualized text pure-text queries modality gap semantic content rendering variations