CROP: Expert-Aligned Image Cropping via Compositional Reasoning and Optimizing Preference

• URL: http://arxiv.org/abs/2605.12545v1
• Date: Sat, 09 May 2026 10:21:51 GMT
• Title: CROP: Expert-Aligned Image Cropping via Compositional Reasoning and Optimizing Preference
• Abstract summary: Aesthetic image cropping aims to enhance the aesthetic quality of an image by improving its composition through spatial cropping.<n>Previous methods often rely on saliency prediction or retrieval augmentation, ignoring the task's core requirement: a deep understanding of composition and aesthetics.<n>We propose a novel paradigm that reformulates aesthetic cropping as a multimodal reasoning task, aiming to activate the VLM's analytical and comprehension capabilities in aesthetics.
• Score: 8.129228105494434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aesthetic image cropping aims to enhance the aesthetic quality of an image by improving its composition through spatial cropping. Previous methods often rely on saliency prediction or retrieval augmentation, ignoring the task's core requirement: a deep understanding of composition and aesthetics. Consequently, saliency-based methods struggle to make compositional trade-offs in complex scenes, while retrieval-based methods blindly refer to similar cases, lacking adaptive reasoning for unique scenes. Both approaches fail to align their automated cropping results with those of human experts. To address the above issues, we propose a novel paradigm that reformulates aesthetic cropping as a multimodal reasoning task, aiming to activate the VLM's analytical and comprehension capabilities in aesthetics. We design a Compositional Reasoning and Optimizing Preference method (CROP) that directs the VLM to think like a professional photographer. It deconstructs a complex and subjective aesthetic problem into an "analysis-proposal-decision" process, reasoning step by step through the analysis of scene elements and compositional principles. Meanwhile, our expert preference alignment module makes the model's decision consistent with human expert aesthetics. Extensive experiments across multiple datasets validate our method's superiority and component effectiveness.

Related papers

• Advancing Aesthetic Image Generation via Composition Transfer [57.624601729777986]
  Composer is a framework designed to model composition in a semantic-agnostic manner.<n>It supports composition transfer by extracting key composition-aware representations from a reference image.<n>It also supports theme-driven composition retrieval by leveraging the in-context learning capabilities of Large Vision-Language Models.
  arXiv  Detail & Related papers  (2026-05-06T07:56:16Z)
• Bridging Cognitive Gap: Hierarchical Description Learning for Artistic Image Aesthetics Assessment [51.40989269202702]
  aesthetic quality assessment task is crucial for developing a human-aligned quantitative evaluation system for AIGC.<n>We propose ArtQuant, an aesthetics assessment framework for artistic images which couples isolated aesthetic dimensions through description generation.<n>Our approach achieves epoch state-of-the-art performance on several datasets while requiring only 33% of conventional trainings.
  arXiv  Detail & Related papers  (2025-12-29T12:18:26Z)
• AesCrop: Aesthetic-driven Cropping Guided by Composition [4.402109574578595]
  AesCrop is a composition-aware hybrid image-cropping model that integrates a VMamba image encoder, augmented with a novel Mamba Composition Attention Bias (MCAB)<n>MCAB encodes compositional cues into the attention mechanism, directing AesCrop to focus on the most compositionally salient regions.<n>Extensive experiments demonstrate that AesCrop outperforms current state-of-the-art methods, delivering superior quantitative metrics and qualitatively more pleasing crops.
  arXiv  Detail & Related papers  (2025-10-26T04:30:02Z)
• ProCrop: Learning Aesthetic Image Cropping from Professional Compositions [57.949730056500634]
  ProCrop is a retrieval-based method that leverages professional photography to guide cropping decisions.<n>We present a large-scale dataset of 242K weakly-annotated images, generated by out-painting professional images.<n>This composition-aware dataset generation offers diverse high-quality crop proposals guided by aesthetic principles.
  arXiv  Detail & Related papers  (2025-05-28T15:38:44Z)
• Photography Perspective Composition: Towards Aesthetic Perspective Recommendation [22.780632515229286]
  Traditional photography composition approaches are dominated by 2D cropping-based methods.<n>Professional photographers often employ perspective adjustment as a form of 3D recomposition.<n>We propose photography perspective composition (PPC), extending beyond traditional cropping-based methods.
  arXiv  Detail & Related papers  (2025-05-27T03:04:48Z)
• FreeCompose: Generic Zero-Shot Image Composition with Diffusion Prior [50.0535198082903]
  We offer a novel approach to image composition, which integrates multiple input images into a single, coherent image. We showcase the potential of utilizing the powerful generative prior inherent in large-scale pre-trained diffusion models to accomplish generic image composition.
  arXiv  Detail & Related papers  (2024-07-06T03:35:43Z)
• Cones 2: Customizable Image Synthesis with Multiple Subjects [50.54010141032032]
  We study how to efficiently represent a particular subject as well as how to appropriately compose different subjects. By rectifying the activations in the cross-attention map, the layout appoints and separates the location of different subjects in the image.
  arXiv  Detail & Related papers  (2023-05-30T18:00:06Z)
• Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions [0.0]
  Methods: Novel enhancement algorithms based on color processing and visual illusions are compared to classic methods in a user experience experiment. Users exhibited a broad behavioral spectrum, under the influence of factors such as personality and social conditioning, tasks and application domains, expertise level and image quality, and affordances of software, hardware, and interfaces.
  arXiv  Detail & Related papers  (2021-03-11T23:48:17Z)
• Adversarial Image Composition with Auxiliary Illumination [53.89445873577062]
  We propose an Adversarial Image Composition Net (AIC-Net) that achieves realistic image composition. A novel branched generation mechanism is proposed, which disentangles the generation of shadows and the transfer of foreground styles. Experiments on pedestrian and car composition tasks show that the proposed AIC-Net achieves superior composition performance.
  arXiv  Detail & Related papers  (2020-09-17T12:58:16Z)

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.