# Deep Cropping via Attention Box Prediction and Aesthetics Assessment

arXiv: https://arxiv.org/abs/1710.08014 · HTML: https://mlsanity.com/paper/1710.08014/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2017.

## Built on by (most cited first, 17 of 17)

- [Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding](https://mlsanity.com/paper/2303.09706/) (2023)
- [Learning to Detect Instance-level Salient Objects Using Complementary Image Labels](https://mlsanity.com/paper/2111.10137/) (2021)
- [Spatial-Semantic Collaborative Cropping for User Generated Content](https://mlsanity.com/paper/2401.08086/) (2024)
- [Human-centric Image Cropping with Partition-aware and Content-preserving Features](https://mlsanity.com/paper/2207.10269/) (2022)
- [PhotoFramer: Multi-modal Image Composition Instruction](https://mlsanity.com/paper/2512.00993/) (2025)
- [ProCrop: Learning Aesthetic Image Cropping from Professional Compositions](https://mlsanity.com/paper/2505.22490/) (2025)
- [Camera View Adjustment Prediction for Improving Image Composition](https://mlsanity.com/paper/2104.07608/) (2021)
- [An Experience-based Direct Generation approach to Automatic Image Cropping](https://mlsanity.com/paper/2212.14561/) (2022)
- [Aesthetic Photo Collage with Deep Reinforcement Learning](https://mlsanity.com/paper/2110.09775/) (2021)
- [Image Cropping under Design Constraints](https://mlsanity.com/paper/2310.08892/) (2023)
- [Efficient Multi-Crop Saliency Partitioning for Automatic Image Cropping](https://mlsanity.com/paper/2506.22814/) (2025)
- [Global Attention-Fused Image Cropping with Attention-Guided and Global-Aligned Crop Evaluator](https://mlsanity.com/paper/2608.04821/) (2026)
- [Towards Robust Unsupervised Attention Prediction in Autonomous Driving](https://mlsanity.com/paper/2501.15045/) (2025)
- [SSNet: Saliency Prior and State Space Model-based Network for Salient Object Detection in RGB-D Images](https://mlsanity.com/paper/2503.02270/) (2025)
- [Clutter Detection and Removal by Multi-Objective Analysis for Photographic Guidance](https://mlsanity.com/paper/2507.14553/) (2025)
- [Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing](https://mlsanity.com/paper/2604.24947/) (2026)
- [FrameLite: Decision-Oriented Aesthetic Cropping with a Compact Vision-Language Model](https://mlsanity.com/paper/2610.17639/) (2026)

## Abstract (arXiv)

We model the photo cropping problem as a cascade of attention box regression and aesthetic quality classification, based on deep learning. A neural network is designed that has two branches for predicting attention bounding box and analyzing aesthetics, respectively. The predicted attention box is treated as an initial crop window where a set of cropping candidates are generated around it, without missing important information. Then, aesthetics assessment is employed to select the final crop as the one with the best aesthetic quality. With our network, cropping candidates share features within full-image convolutional feature maps, thus avoiding repeated feature computation and leading to higher computation efficiency. Via leveraging rich data for attention prediction and aesthetics assessment, the proposed method produces high-quality cropping results, even with the limited availability of training data for photo cropping. The experimental results demonstrate the competitive results and fast processing speed (5 fps with all steps).