# ProCrop: Learning Aesthetic Image Cropping from Professional Compositions

arXiv: https://arxiv.org/abs/2505.22490 · HTML: https://mlsanity.com/paper/2505.22490/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 59 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 28 of 28)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [Conditional DETR for Fast Training Convergence](https://mlsanity.com/paper/2108.06152/) (2021)
- [Highly Accurate Dichotomous Image Segmentation](https://mlsanity.com/paper/2203.03041/) (2022)
- [Composition-aware Graphic Layout GAN for Visual-textual Presentation Designs](https://mlsanity.com/paper/2205.00303/) (2022)
- [Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation](https://mlsanity.com/paper/2311.13602/) (2023)
- [Reliable and Efficient Image Cropping: A Grid Anchor based Approach](https://mlsanity.com/paper/1904.04441/) (2019)
- [Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative Study](https://mlsanity.com/paper/1701.01480/) (2017)
- [Grid Anchor based Image Cropping: A New Benchmark and An Efficient Model](https://mlsanity.com/paper/1909.08989/) (2019)
- [Learning to Compose with Professional Photographs on the Web](https://mlsanity.com/paper/1702.00503/) (2017)
- [Image Cropping with Composition and Saliency Aware Aesthetic Score Map](https://mlsanity.com/paper/1911.10492/) (2019)
- [Deep Cropping via Attention Box Prediction and Aesthetics Assessment](https://mlsanity.com/paper/1710.08014/) (2017)
- [Retrieve-Then-Adapt: Example-based Automatic Generation for Proportion-related Infographics](https://mlsanity.com/paper/2008.01177/) (2020)
- [Spatial-Semantic Collaborative Cropping for User Generated Content](https://mlsanity.com/paper/2401.08086/) (2024)
- [Learning Subject-Aware Cropping by Outpainting Professional Photos](https://mlsanity.com/paper/2312.12080/) (2023)
- [Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression](https://mlsanity.com/paper/1712.09048/) (2017)
- [Human-centric Image Cropping with Partition-aware and Content-preserving Features](https://mlsanity.com/paper/2207.10269/) (2022)
- [Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition](https://mlsanity.com/paper/2309.12042/) (2023)
- [PosSAM: Panoptic Open-vocabulary Segment Anything](https://mlsanity.com/paper/2403.09620/) (2024)
- [Listwise View Ranking for Image Cropping](https://mlsanity.com/paper/1905.05352/) (2019)
- [Semantic Line Combination Detector](https://mlsanity.com/paper/2404.18399/) (2024)

## Built on by (most cited first, 8 of 8)

- [PhotoFramer: Multi-modal Image Composition Instruction](https://mlsanity.com/paper/2512.00993/) (2025)
- [Venus: Benchmarking and Empowering Multimodal Large Language Models for Aesthetic Guidance and Cropping](https://mlsanity.com/paper/2602.23980/) (2026)
- [Silhouette-based Gait Foundation Model](https://mlsanity.com/paper/2512.00691/) (2025)
- [CROP: Expert-Aligned Image Cropping via Compositional Reasoning and Optimizing Preference](https://mlsanity.com/paper/2605.12545/) (2026)
- [ShutterMuse: Capture-Time Photography Guidance with MLLMs](https://mlsanity.com/paper/2606.25763/) (2026)
- [ShotCrop3: Cropping Human-Centric Images into Cinematic Triple-Shot Compositions](https://mlsanity.com/paper/2606.05635/) (2026)
- [Discrete Annotation, Continuous Preference: Rethinking Supervision for Accurate and Generalizable Aesthetic Image Cropping](https://mlsanity.com/paper/2610.00582/) (2026)
- [FrameLite: Decision-Oriented Aesthetic Cropping with a Compact Vision-Language Model](https://mlsanity.com/paper/2610.17639/) (2026)

## Abstract (arXiv)

Image cropping is crucial for enhancing the visual appeal and narrative impact of photographs, yet existing rule-based and data-driven approaches often lack diversity or require annotated training data. We introduce ProCrop, a retrieval-based method that leverages professional photography to guide cropping decisions. By fusing features from professional photographs with those of the query image, ProCrop learns from professional compositions, significantly boosting performance. Additionally, we present a large-scale dataset of 242K weakly-annotated images, generated by out-painting professional images and iteratively refining diverse crop proposals. This composition-aware dataset generation offers diverse high-quality crop proposals guided by aesthetic principles and becomes the largest publicly available dataset for image cropping. Extensive experiments show that ProCrop significantly outperforms existing methods in both supervised and weakly-supervised settings. Notably, when trained on the new dataset, our ProCrop surpasses previous weakly-supervised methods and even matches fully supervised approaches. Both the code and dataset will be made publicly available to advance research in image aesthetics and composition analysis.