A scalable evaluation framework for simile understanding is proposed that includes a controlled simile dataset in which metaphorical vehicles are drawn from a predefined set of object-detectable categories and combined with diverse templates.
Abstract
Similes provide a compact and expressive way to describe visual characteristics in text prompts. Recent text-to-image models (t2i models) can produce visually compelling outputs from simile prompts, yet even frontier models frequently misinterpret the metaphorical vehicle and confuse it with the object. These systematic failures reveal a gap between figurative language and object-level visual grounding in t2i models. To investigate this issue, we propose a scalable evaluation framework for simile understanding. Our framework includes (1) a controlled simile dataset in which metaphorical vehicles are drawn from a predefined set of object-detectable categories and combined with diverse templates, (2) automatic grounding metrics based on YOLO (You Only Look Once) detection, and (3) text encoder layer analysis using Diffusion Lens to track how metaphorical vehicles emerge during generation. Experiments across architecturally diverse t2i models reveal consistent literalization failure patterns. We further discuss potential mitigation strategies for improving simile grounding in t2i models.
In recent years, text-to-image (T2I) generation models have made substantial progress, particularly in visual realism and the expression of prompt semantics. However, a key difficulty remains: how to evaluate generated results automatically in a way that is both comprehensive and interpretable, while still being practical for real deployment. To address this issue, this paper proposes a multi-dimensional image quality assessment framework for T2I tasks. The framework examines generated images from five dimensions—text fidelity, perceptual quality, object consistency, relational consistency, and global semantic alignment—and derives a final quality score through normalization and weighted fusion. In terms of methodology, the framework combines Tesseract OCR, perceptual quality analysis based on Laplacian variance and exposure statistics, YOLO object detection, BLIP-based visual question answering, and CLIP image-text similarity, thereby forming a modular evaluation pipeline with diagnostic capability. Experiments on multiple mainstream T2I models and representative prompts show that the proposed method can not only distinguish overall performance differences across models, but also provide interpretable results at the level of individual dimensions.
Text-to-image (T2I) models have achieved remarkable success at faithfully rendering specified objects and attributes, yet their ability to produce visual metaphors, images that convey abstract ideas by combining elements from two distinct domains, remains largely unexamined. To bridge this gap, we introduce VMetaphor-Bench, the first benchmark for evaluating visual metaphor generation in T2I models. It comprises 1,500 visual metaphors curated from real-world creative imagery, organized into three levels and ten categories, with each sample paired with two prompts of differing specificity. For evaluation, we develop a hybrid framework within an MLLM-as-judge paradigm, combining a multiple-choice question (MCQ) based protocol of 9,594 questions across four levels of metaphorical fidelity with a dimension-based scoring protocol along three perceptual dimensions. Extensive evaluation of 11 representative T2I models reveals that even the strongest proprietary models struggle with compositional structuring and cross-domain mapping, key aspects of metaphorical expression, highlighting visual metaphor generation as an important frontier for future T2I research.
Chuer Chen, Zi-Chen Wang, Yi He et al.· 0 citations
This work introduces three tasks/objectives for reverse localization of text as an instruction-tuning mechanism, where the model is guided to extract textual content based on spatial localization cues, thereby enhancing its spatial grounding ability.
Recent advances in text-to-image (T2I) models have revolutionized the field of image generation and editing. However, identifying semantics that a T2I model can successfully edit in an image continues to be a challenging task. Most existing approaches require users to manually specify semantics to modify a particular image, a time-consuming process that often involves extensive trial and error. In this paper, we present RankT2I, a novel, training-free, and model-agnostic framework that automates the discovery of editable semantics in diffusion and FLUX-based models. Given a visual domain, we first utilize a multimodal vision-language model to gather a broad set of candidate semantics. We then frame semantic discovery as a set selection problem and use a submodular objective to identify semantics that are relevant, editable, and diverse. Our method helps users efficiently identify a wide range of semantics for text-to-image editing models across several domains while outperforming existing methods.
It is shown that VQA can serve as an effective semantic feedback to significantly enhance prompt-image alignment without retraining the diffusion model, providing a powerful, interpretable and self-correcting strategy for text-to-image production.
