# A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment

arXiv: https://arxiv.org/abs/1704.00248 · HTML: https://mlsanity.com/paper/1704.00248/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2017.

## Built on by (most cited first, 28 of 28)

- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [Differentiable Patch Selection for Image Recognition](https://mlsanity.com/paper/2104.03059/) (2021)
- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method](https://mlsanity.com/paper/2303.15166/) (2023)
- [UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark](https://mlsanity.com/paper/2404.09619/) (2024)
- [Image Composition Assessment with Saliency-augmented Multi-pattern Pooling](https://mlsanity.com/paper/2104.03133/) (2021)
- [Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images](https://mlsanity.com/paper/2303.08050/) (2023)
- [Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment](https://mlsanity.com/paper/2206.08614/) (2022)
- [A Hypothesis for the Aesthetic Appreciation in Neural Networks](https://mlsanity.com/paper/2108.02646/) (2021)
- [Composition and Style Attributes Guided Image Aesthetic Assessment](https://mlsanity.com/paper/2111.04647/) (2021)
- [User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning](https://mlsanity.com/paper/2106.07488/) (2021)
- [Image Aesthetics Assessment Using Graph Attention Network](https://mlsanity.com/paper/2206.12869/) (2022)
- [Aesthetic Attribute Assessment of Images Numerically on Mixed Multi-attribute Datasets](https://mlsanity.com/paper/2207.01806/) (2022)
- [ALL-E: Aesthetics-guided Low-light Image Enhancement](https://mlsanity.com/paper/2304.14610/) (2023)
- [Neural Image Beauty Predictor Based on Bradley-Terry Model](https://mlsanity.com/paper/2111.10127/) (2021)
- [Aesthetic Photo Collage with Deep Reinforcement Learning](https://mlsanity.com/paper/2110.09775/) (2021)
- [AID-AppEAL: Automatic Image Dataset and Algorithm for Content Appeal Enhancement and Assessment Labeling](https://mlsanity.com/paper/2407.05546/) (2024)
- [DocReward: A Document Reward Model for Structuring and Stylizing](https://mlsanity.com/paper/2510.11391/) (2025)
- [DiffoR: A Unified Continuous Generative Framework for Universal Ordinal Regression](https://mlsanity.com/paper/2606.07599/) (2026)
- [Multi-Modal Aesthetic Assessment for MObile Gaming Image](https://mlsanity.com/paper/2101.11700/) (2021)
- [Considering user agreement in learning to predict the aesthetic quality](https://mlsanity.com/paper/2110.06956/) (2021)
- [Aesthetic Attributes Assessment of Images with AMANv2 and DPC-CaptionsV2](https://mlsanity.com/paper/2208.04522/) (2022)
- [Product Review Image Ranking for Fashion E-commerce](https://mlsanity.com/paper/2308.05390/) (2023)
- [QUASAR: QUality and Aesthetics Scoring with Advanced Representations](https://mlsanity.com/paper/2403.06866/) (2024)
- [AACP: Aesthetics assessment of children's paintings based on self-supervised learning](https://mlsanity.com/paper/2403.07578/) (2024)
- [Explaining Automatic Image Assessment](https://mlsanity.com/paper/2502.01873/) (2025)
- [LMHLD: A Large-scale Multi-source High-resolution Landslide Dataset for Landslide Detection based on Deep Learning](https://mlsanity.com/paper/2502.19866/) (2025)
- [Learning to Look before Learning to Like: Incorporating Human Visual Cognition into Aesthetic Quality Assessment](https://mlsanity.com/paper/2604.15853/) (2026)

## Abstract (arXiv)

Deep convolutional neural networks (CNN) have recently been shown to generate promising results for aesthetics assessment. However, the performance of these deep CNN methods is often compromised by the constraint that the neural network only takes the fixed-size input. To accommodate this requirement, input images need to be transformed via cropping, warping, or padding, which often alter image composition, reduce image resolution, or cause image distortion. Thus the aesthetics of the original images is impaired because of potential loss of fine grained details and holistic image layout. However, such fine grained details and holistic image layout is critical for evaluating an image's aesthetics. In this paper, we present an Adaptive Layout-Aware Multi-Patch Convolutional Neural Network (A-Lamp CNN) architecture for photo aesthetic assessment. This novel scheme is able to accept arbitrary sized images, and learn from both fined grained details and holistic image layout simultaneously. To enable training on these hybrid inputs, we extend the method by developing a dedicated double-subnet neural network structure, i.e. a Multi-Patch subnet and a Layout-Aware subnet. We further construct an aggregation layer to effectively combine the hybrid features from these two subnets. Extensive experiments on the large-scale aesthetics assessment benchmark (AVA) demonstrate significant performance improvement over the state-of-the-art in photo aesthetic assessment.