PixLens: A Novel Framework for Disentangled Evaluation in Diffusion-Based Image Editing with Object Detection + SAM

• URL: http://arxiv.org/abs/2410.05710v1
• Date: Tue, 8 Oct 2024 06:05:15 GMT
• Title: PixLens: A Novel Framework for Disentangled Evaluation in Diffusion-Based Image Editing with Object Detection + SAM
• Authors: Stefan Stefanache, Lluís Pastor Pérez, Julen Costa Watanabe, Ernesto Sanchez Tejedor, Thomas Hofmann, Enis Simsar,
• Abstract summary: evaluating diffusion-based image-editing models is a crucial task in the field of Generative AI. Our benchmark, PixLens, provides a comprehensive evaluation of both edit quality and latent representation disentanglement.
• Score: 17.89238060470998
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evaluating diffusion-based image-editing models is a crucial task in the field of Generative AI. Specifically, it is imperative to assess their capacity to execute diverse editing tasks while preserving the image content and realism. While recent developments in generative models have opened up previously unheard-of possibilities for image editing, conducting a thorough evaluation of these models remains a challenging and open task. The absence of a standardized evaluation benchmark, primarily due to the inherent need for a post-edit reference image for evaluation, further complicates this issue. Currently, evaluations often rely on established models such as CLIP or require human intervention for a comprehensive understanding of the performance of these image editing models. Our benchmark, PixLens, provides a comprehensive evaluation of both edit quality and latent representation disentanglement, contributing to the advancement and refinement of existing methodologies in the field.

Related papers

• Stable Flow: Vital Layers for Training-Free Image Editing [74.52248787189302]
  Diffusion models have revolutionized the field of content synthesis and editing. Recent models have replaced the traditional UNet architecture with the Diffusion Transformer (DiT) We propose an automatic method to identify "vital layers" within DiT, crucial for image formation. Next, to enable real-image editing, we introduce an improved image inversion method for flow models.
  arXiv  Detail & Related papers  (2024-11-21T18:59:51Z)
• Learning Action and Reasoning-Centric Image Editing from Videos and Simulations [45.637947364341436]
  AURORA dataset is a collection of high-quality training data, human-annotated and curated from videos and simulation engines. We evaluate an AURORA-finetuned model on a new expert-curated benchmark covering 8 diverse editing tasks. Our model significantly outperforms previous editing models as judged by human raters.
  arXiv  Detail & Related papers  (2024-07-03T19:36:33Z)
• Ground-A-Score: Scaling Up the Score Distillation for Multi-Attribute Editing [49.419619882284906]
  Ground-A-Score is a powerful model-agnostic image editing method by incorporating grounding during score distillation. The selective application with a new penalty coefficient and contrastive loss helps to precisely target editing areas. Both qualitative assessments and quantitative analyses confirm that Ground-A-Score successfully adheres to the intricate details of extended and multifaceted prompts.
  arXiv  Detail & Related papers  (2024-03-20T12:40:32Z)
• Diffusion Model-Based Image Editing: A Survey [46.244266782108234]
  Denoising diffusion models have emerged as a powerful tool for various image generation and editing tasks. We provide an exhaustive overview of existing methods using diffusion models for image editing. To further evaluate the performance of text-guided image editing algorithms, we propose a systematic benchmark, EditEval.
  arXiv  Detail & Related papers  (2024-02-27T14:07:09Z)
• Advancing Generative Model Evaluation: A Novel Algorithm for Realistic Image Synthesis and Comparison in OCR System [1.2289361708127877]
  This research addresses a critical challenge in the field of generative models, particularly in the generation and evaluation of synthetic images. We introduce a pioneering algorithm to objectively assess the realism of synthetic images. Our algorithm is particularly tailored to address the challenges in generating and evaluating realistic images of Arabic handwritten digits.
  arXiv  Detail & Related papers  (2024-02-27T04:53:53Z)
• Counterfactual Image Editing [54.21104691749547]
  Counterfactual image editing is an important task in generative AI, which asks how an image would look if certain features were different. We formalize the counterfactual image editing task using formal language, modeling the causal relationships between latent generative factors and images. We develop an efficient algorithm to generate counterfactual images by leveraging neural causal models.
  arXiv  Detail & Related papers  (2024-02-07T20:55:39Z)
• High-Fidelity Diffusion-based Image Editing [19.85446433564999]
  The editing performance of diffusion models tends to be no more satisfactory even with increasing denoising steps. We propose an innovative framework where a Markov module is incorporated to modulate diffusion model weights with residual features. We introduce a novel learning paradigm aimed at minimizing error propagation during the editing process, which trains the editing procedure in a manner similar to denoising score-matching.
  arXiv  Detail & Related papers  (2023-12-25T12:12:36Z)
• Diffusion Models for Image Restoration and Enhancement -- A Comprehensive Survey [96.99328714941657]
  We present a comprehensive review of recent diffusion model-based methods on image restoration. We classify and emphasize the innovative designs using diffusion models for both IR and blind/real-world IR. We propose five potential and challenging directions for the future research of diffusion model-based IR.
  arXiv  Detail & Related papers  (2023-08-18T08:40:38Z)
• 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.