Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:50:45.594128Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:2507.08039.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:50:45.594128Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
56 of 56 outbound references displayed
External citation measurements
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Observation 672c7f4d-1925-4abf-a807-390d7efa6e58 · outbound
Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Spice: Semantic propositional image caption evaluation
Reference 1
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models dSprites: Disentanglement testing Sprites dataset
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Isolating sources of disentanglement in variational autoencoders
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models TIGEr: Text-to-Image Grounding for Image Caption Evaluation
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Semantic object accuracy for generative text-to-image synthesis
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Denoising diffusion probabilistic models
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models ViLBERTScore: Evaluating Image Caption Using Vision-and-Language BERT
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models CLIPScore: A Reference-free Evaluation Metric for Image Captioning
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Learning transferable visual models from natural language supervision
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models PaLI: A Jointly-Scaled Multilingual Language-Image Model
Reference 10
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Scaling Instruction-Finetuned Language Models
Reference 11
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Tise: Bag of metrics for text-to-image synthesis evaluation
Reference 12
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Benchmarking Spatial Relationships in Text-to-Image Generation
Reference 13
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Perceptual artifacts localization for inpainting
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models X-IQE: eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models
Reference 16
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-image Generation
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback
Reference 19
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Stable Diffusion 3.5 Release
Reference 20
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Explaining Latent Representations Of Generative Models With Large Multimodal Models
Reference 21
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multi-modal Foundation Models
Reference 22
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Scaling rectified flow transformers for high-resolution image synthesis
Reference 23
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models TIAM-A metric for evaluating alignment in Text-to-Image generation
Reference 24
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models A Survey on Quality Metrics for Text-to-Image Generation
Reference 25
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models MMGenBench: Fully Automatically Evaluating LMMs from the Text-to-Image Generation Perspective
Reference 26
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Evaluating Text-to-Visual Generation with Image-to-Text Generation
Reference 27
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language Models
Reference 28
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation
Reference 29
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models What you see is what you read? improving text-image alignment evaluation
Reference 30
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models square" at 64x64 resolution 12 Stable Diffusion 3 Medium Stable Diffusion 3.5 Large Stable Diffusion 3.5 Large Turbo Figure 8: Sample images for prompt
Reference 32
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Reference 33
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Reference 34
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models 9.2 Hardware The section describes the hardware requirements to conduct our experiments successfully
Reference 35
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Reference 36
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Reference 39
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models A combination of these 6 factors can completely define each image
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Each value represents the exact pixel value in the 256 x 256 image
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Reference 43
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Every possible combination of these factors lead to a 9216 image test dataset
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Reference 47
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models This can be readily accomplished with statistical tests
Reference 48
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Reference 49
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models We observe only 1 factor of variation with change in values
Reference 51
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Towards Evaluating Robustness of Prompt Adherence in Text to Image Models Dimensions in this category form sub-groups which encode a particular factor of variation
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Reference 54
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Reference 56
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No inbound Pith citation observations are available.