Source-linked AI summary
Revealing the Impact of Visual Text Style on Attribute-based Descriptions Produced by Large Visual Language Models
Xiaomeng Wang, Martha Larson, Zhengyu Zhao
TL;DR
This paper asks whether visual text style affects an LVLM’s attribute descriptions when the concept is correctly identified and style should be irrelevant. It compares functional and decorative styles and finds that descriptions differ across them, motivating style-aware evaluation.
Problem
Whether visual text style affects LVLM attribute descriptions remains a question beyond OCR, since a robust response should disregard irrelevant style.
Source: §1 · pp. 1–2Method
The authors compare attribute-description distributions for functional and decorative visual text, using concepts whose text the LVLM correctly identifies and varying prompts, runs, and rendering configurations.
Source: §3.1 · p. 2Results
Takeaways & Limitations
The findings motivate incorporating style variation into evaluation protocols for multimedia systems deployed in text-rich visual environments.
Source: §5 · p. 4More work is needed to confirm whether visual text style introduces an emotion-related bias.
Source: §5 · p. 4
Abstract
from arXiv · showhide
When the visual style of text is considered, a wide variety can be observed in font, color, and size. However, when a word is read, its meaning is independent of the style in which it has been written or rendered. In this paper, we investigate whether, and how, the style in which a word is visualized in an image impacts the description that a Large Visual Language Model (LVLM) provides for the concept to which that word refers. Specifically, we investigate how functional text styles (readability-oriented, e.g., black sans-serif) versus decorative styles (display-oriented, e.g., colored cursive/script) affect LVLMs' descriptions of a concept in terms of the attributes of that concept. Our experiments study the situation in which the LVLM is able to correctly identify the concept referred to by a visual text, i.e., by a word or words rendered as an image, and in which the visual text style should not influence the attribute-based description that the LVLM produces. Our experimental results reveal that even when the concept is correctly identified, text style influences the model's attribute-based descriptions of the concept. Our findings demonstrate non-trivial style leakage from text style into semantic inference and motivate style-aware evaluation and mitigation for LVLM-based multimedia systems.
1 Introduction
The paper asks whether visual text style affects LVLMs’ attribute descriptions beyond OCR, focusing on cases where the concept is correctly identified and style should be irrelevant.
1 Introduction
- The paper examines how style affects LVLM interpretation of visual text semantics, going beyond OCR performance.Source: §1 · p. 1
- The study contrasts functional styles, represented by black sans-serif text, with decorative styles represented by colored cursive or script fonts.The authors focus on cases where text style is irrelevant and a correct response should use the word’s meaning alone.Source: §1 · p. 1
- The authors focus on attribute-based descriptions because they reflect how a model captures concept semantics.Source: §1 · p. 1
- Style-related instability in associating visual text with concept attributes could propagate to downstream reasoning and retrieval, affecting overall reliability.Source: §1 · p. 2
- Even when LVLMs correctly identify a concept in visual text, their attribute-based descriptions are not style-invariant.The study compares descriptions prompted by functional and decorative styles.Source: §1 · p. 2
2 Related Work
Prior work reports inconsistencies between textual and visual inputs, including visual text, even when recognition succeeds; this paper examines style as an additional axis within visual text.
2 Related Work
- PRISM reports that LVLMs often perform substantially better on queries delivered as text than on equivalent visually rendered queries.Source: §2 · p. 2
- REST/REST+ [6] finds that closing the OCR gap is insufficient to resolve inconsistencies across equivalent text and image or visual-text inputs.Source: §2 · p. 2
- POPVQA [2] reports factual performance drops under visual reference despite correct recognition, while [1] reports degradation in factual association recall and correlated internal states.Source: §2 · p. 2
- This paper studies inconsistency within the visual modality by examining text presented visually.Source: §2 · p. 2
3 Evaluating the Impact of Visual Text Style
The evaluation compares attribute descriptions generated from functional and decorative visual text, filtering for successful recognition before analyzing style-related distribution differences.
3 Evaluating the Impact of Visual Text Style
- The evaluation creates functional- and decorative-style images for each concept, filters visual texts to those the LVLM identifies correctly, and compares the resulting attribute distributions.Source: §3 · p. 2
3.1 Collecting Attribute-based Descriptions
- Five semantically equivalent prompt phrasings, repeated context-free runs, and rendering configurations varying font size and position address sources of response variation.Source: §3.1 · p. 2
3.2 Comparing Attribute-based Descriptions
- The authors merge attribute lists by style and concept, form a shared vocabulary, and use Total Variation distance to quantify distribution differences.TV ranges from 0 to 1, with larger values indicating larger differences.Source: §3.2 · p. 2
- A Pearson chi-squared homogeneity test evaluates whether the two styles induce the same attribute distribution, merging terms with total counts below τ into a bin.Source: §3.2 · p. 2
- The authors also compare style-specific ranked attribute lists to identify terms appearing among the top-N attributes in one style but not the other.Source: §3.2 · p. 2
4 Experiments
The experiments compare attribute descriptions of cat and dog breeds from two LVLMs after filtering for successful visual-text identification. Even with OCR success, attribute distributions differ significantly between functional and decorative styles, with Qwen showing greater style sensitivity under a stated comparability assumption.
4.1 Experimental Setup
- The study uses Qwen2.5-VL-3B-Instruct and GPT-4o-mini on Oxford-IIIT Pet breed labels rendered in functional or decorative styles, retaining breeds with adequate correctly identified images.The setup uses eight regular sans-serif fonts for functional text and eight script or cursive fonts with randomized colors for decorative text; 32 breeds remain after filtering.Source: §4.1 · p. 3
- For each concept and style, the models receive five semantically varied prompts, each repeated five times independently, yielding 900 attribute lists per model.The collection uses 36 visual texts per concept and style; an auxiliary LLM standardizes Qwen outputs into adjective or phrasal-descriptor lists.Source: §4.1 · p. 3
4.2 Experimental Results
- Across breeds, functional-versus-decorative attribute distributions differ significantly for all cases (p < 0.001).Figure 2 reports the breed-level TV values; the authors caution against over-interpreting exact differences between breeds because each concept has its own vocabulary.Source: §4.2 · p. 4, Figure 2 · p. 4
- Assuming breed sample spaces are relatively comparable, Qwen2.5-VL-3B-Instruct exhibits greater style sensitivity than GPT-4o-mini.The authors caution that each concept's TV distance is calculated over a different sample space.Source: §4.2 · p. 4
- Across-style average pairwise TV distance exceeds within-style distance for both models, supporting style differences rather than font choice or general vocabulary variation as the source of the shift.Figure 3 compares average pairwise distances among fonts within the same style against distances between fonts of different styles.Source: §4.2 · p. 4, Figure 3 · p. 4
- For both models, top attributes are predominantly temperamental, but physical attributes among the top three are more likely in functional than decorative descriptions.For Qwen, the top-three attributes differ across styles for 13 of 32 breeds; in five cases, a physical attribute replaces a temperamental one.Source: §4.2 · p. 4
5 Conclusion and Outlook
LVLMs’ attribute predictions can lack style invariance even when they correctly identify a concept from visual text. The findings motivate including style variation in future evaluations of multimedia systems in text-rich visual environments.
5 Conclusion and Outlook
- LVLMs can correctly identify concepts from visual text while producing attribute predictions that lack style invariance.The paper describes this as style leakage from visual text into semantic inference.Source: §5 · p. 4
- The findings motivate incorporating style variation into future evaluation protocols for multimedia systems deployed in text-rich visual environments.Source: §5 · p. 4
- Decorative text may connect more strongly than functional text to temperamental attributes, but more work is needed to confirm an emotion-related bias.The paper links temperament to pet bonds and decorative style to emotional reactions to visual text.Source: §5 · p. 4