Interactive Visual Assessment for Text-to-Image Generation Models

• URL: http://arxiv.org/abs/2411.15509v1
• Date: Sat, 23 Nov 2024 10:06:18 GMT
• Title: Interactive Visual Assessment for Text-to-Image Generation Models
• Authors: Xiaoyue Mi, Fan Tang, Juan Cao, Qiang Sheng, Ziyao Huang, Peng Li, Yang Liu, Tong-Yee Lee,
• Abstract summary: We propose DyEval, a dynamic interactive visual assessment framework for generative models. DyEval features an intuitive visual interface that enables users to interactively explore and analyze model behaviors. Our framework provides valuable insights for improving generative models and has broad implications for advancing the reliability and capabilities of visual generation systems.
• Score: 28.526897072724662
• License:
• Abstract: Visual generation models have achieved remarkable progress in computer graphics applications but still face significant challenges in real-world deployment. Current assessment approaches for visual generation tasks typically follow an isolated three-phase framework: test input collection, model output generation, and user assessment. These fashions suffer from fixed coverage, evolving difficulty, and data leakage risks, limiting their effectiveness in comprehensively evaluating increasingly complex generation models. To address these limitations, we propose DyEval, an LLM-powered dynamic interactive visual assessment framework that facilitates collaborative evaluation between humans and generative models for text-to-image systems. DyEval features an intuitive visual interface that enables users to interactively explore and analyze model behaviors, while adaptively generating hierarchical, fine-grained, and diverse textual inputs to continuously probe the capability boundaries of the models based on their feedback. Additionally, to provide interpretable analysis for users to further improve tested models, we develop a contextual reflection module that mines failure triggers of test inputs and reflects model potential failure patterns supporting in-depth analysis using the logical reasoning ability of LLM. Qualitative and quantitative experiments demonstrate that DyEval can effectively help users identify max up to 2.56 times generation failures than conventional methods, and uncover complex and rare failure patterns, such as issues with pronoun generation and specific cultural context generation. Our framework provides valuable insights for improving generative models and has broad implications for advancing the reliability and capabilities of visual generation systems across various domains.

Related papers

• On the Fairness, Diversity and Reliability of Text-to-Image Generative Models [49.60774626839712]
  multimodal generative models have sparked critical discussions on their fairness, reliability, and potential for misuse. We propose an evaluation framework designed to assess model reliability through their responses to perturbations in the embedding space. Our method lays the groundwork for detecting unreliable, bias-injected models and retrieval of bias provenance.
  arXiv  Detail & Related papers  (2024-11-21T09:46:55Z)
• A Survey on Vision Autoregressive Model [15.042485771127346]
  Autoregressive models have demonstrated impressive performance in natural language processing (NLP) Inspired by their notable success in NLP field, autoregressive models have been intensively investigated recently for computer vision. This paper provides a systematic review of vision autoregressive models, including the development of a taxonomy of existing methods.
  arXiv  Detail & Related papers  (2024-11-13T14:59:41Z)
• Unsupervised Model Diagnosis [49.36194740479798]
  This paper proposes Unsupervised Model Diagnosis (UMO) to produce semantic counterfactual explanations without any user guidance. Our approach identifies and visualizes changes in semantics, and then matches these changes to attributes from wide-ranging text sources.
  arXiv  Detail & Related papers  (2024-10-08T17:59:03Z)
• Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance [61.06245197347139]
  We propose a novel approach to explain the behavior of a black-box model under feature shifts. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation.
  arXiv  Detail & Related papers  (2024-08-24T18:28:19Z)
• A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series [17.08674819906415]
  We introduce HILAD, a novel framework designed to foster a dynamic and bidirectional collaboration between humans and AI. Through our visual interface, HILAD empowers domain experts to detect, interpret, and correct unexpected model behaviors at scale.
  arXiv  Detail & Related papers  (2024-05-06T07:44:07Z)
• Multimodal Large Language Model is a Human-Aligned Annotator for Text-to-Image Generation [87.50120181861362]
  VisionPrefer is a high-quality and fine-grained preference dataset that captures multiple preference aspects. We train a reward model VP-Score over VisionPrefer to guide the training of text-to-image generative models and the preference prediction accuracy of VP-Score is comparable to human annotators.
  arXiv  Detail & Related papers  (2024-04-23T14:53:15Z)
• StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data [129.92449761766025]
  We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models. Our research includes comprehensive experiments conducted on various datasets.
  arXiv  Detail & Related papers  (2023-08-20T12:43:52Z)
• FABRIC: Personalizing Diffusion Models with Iterative Feedback [0.0]
  In an era where visual content generation is increasingly driven by machine learning, the integration of human feedback into generative models presents significant opportunities for enhancing user experience and output quality. We propose FABRIC, a training-free approach applicable to a wide range of popular diffusion models, which exploits the self-attention layer present in the most widely used architectures to condition the diffusion process on a set of feedback images. We show that generation results improve over multiple rounds of iterative feedback through exhaustive analysis, implicitly optimizing arbitrary user preferences.
  arXiv  Detail & Related papers  (2023-07-19T17:39:39Z)
• Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study [71.84852429039881]
  Investigation focuses on the models' ability to handle a range of perturbations, such as sensor faults and noise. We test the generalization and transfer learning capabilities of these models by exposing them to out-of-distribution (OOD) samples.
  arXiv  Detail & Related papers  (2023-06-13T12:43:59Z)
• Counterfactual Edits for Generative Evaluation [0.0]
  We propose a framework for the evaluation and explanation of synthesized results based on concepts instead of pixels. Our framework exploits knowledge-based counterfactual edits that underline which objects or attributes should be inserted, removed, or replaced from generated images. Global explanations produced by accumulating local edits can also reveal what concepts a model cannot generate in total.
  arXiv  Detail & Related papers  (2023-03-02T20:10:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.