# Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment

arXiv: https://arxiv.org/abs/2206.08614 · HTML: https://mlsanity.com/paper/2206.08614/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 69 references that are in the map.
- First posted on arXiv 17 Jun 2022.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [MovieNet: A Holistic Dataset for Movie Understanding](https://mlsanity.com/paper/2007.10937/) (2020)
- [TimeLMs: Diachronic Language Models from Twitter](https://mlsanity.com/paper/2202.03829/) (2022)
- [Photo Aesthetics Ranking Network with Attributes and Content Adaptation](https://mlsanity.com/paper/1606.01621/) (2016)
- [Effective Aesthetics Prediction with Multi-level Spatially Pooled Features](https://mlsanity.com/paper/1904.01382/) (2019)
- [A Unified Framework for Shot Type Classification Based on Subject Centric Lens](https://mlsanity.com/paper/2008.03548/) (2020)
- [A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment](https://mlsanity.com/paper/1704.00248/) (2017)
- [Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment](https://mlsanity.com/paper/2004.03015/) (2020)
- [An Image is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures](https://mlsanity.com/paper/1505.03358/) (2015)
- [Aesthetic Attributes Assessment of Images](https://mlsanity.com/paper/1907.04983/) (2019)
- [Aesthetic Image Captioning From Weakly-Labelled Photographs](https://mlsanity.com/paper/1908.11310/) (2019)
- [Neural Aesthetic Image Reviewer](https://mlsanity.com/paper/1802.10240/) (2018)
- [Composition and Style Attributes Guided Image Aesthetic Assessment](https://mlsanity.com/paper/2111.04647/) (2021)
- [The Document Vectors Using Cosine Similarity Revisited](https://mlsanity.com/paper/2205.13357/) (2022)

## Built on by (most cited first, 15 of 15)

- [ArtiMuse: Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding](https://mlsanity.com/paper/2507.14533/) (2025)
- [JarvisEvo: Towards a Self-Evolving Photo Editing Agent with Synergistic Editor-Evaluator Optimization](https://mlsanity.com/paper/2511.23002/) (2025)
- [The Photographer Eye: Teaching Multimodal Large Language Models to Understand Image Aesthetics like Photographers](https://mlsanity.com/paper/2509.18582/) (2025)
- [Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language Model](https://mlsanity.com/paper/2503.06141/) (2025)
- [Aesthetically Relevant Image Captioning](https://mlsanity.com/paper/2211.15378/) (2022)
- [Aligning Vision Models with Human Aesthetics in Retrieval: Benchmarks and Algorithms](https://mlsanity.com/paper/2406.09397/) (2024)
- [Can MLLMs Critique Like Humans? Evaluating Open-Ended Aesthetic Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2606.29689/) (2026)
- [Aesthetic Image Captioning with Saliency Enhanced MLLMs](https://mlsanity.com/paper/2509.04378/) (2025)
- [Neuroscience-Inspired Analyses of Visual Interestingness in Multimodal Transformers](https://mlsanity.com/paper/2605.08188/) (2026)
- [AI Outperforms Humans in Personalized Image Aesthetics Assessment via LLM-Based Interviews and Semantic Feature Extraction](https://mlsanity.com/paper/2605.14761/) (2026)
- [XPASS-Vis: A Dataset for Cross-Domain Personalized Image Aesthetic Assessment](https://mlsanity.com/paper/2606.15629/) (2026)
- [Learning visual representations for compositional analysis of artworks and photographs](https://mlsanity.com/paper/2608.06142/) (2026)
- [SE-MoLoRA: Shared-Expert LoRA Adapters for Domain-Specific Photographic Assessment](https://mlsanity.com/paper/2608.17514/) (2026)
- [PortraitAes: Intent-Conditioned Structured Portrait Aesthetic Assessment](https://mlsanity.com/paper/2610.05010/) (2026)
- [ScenePercept: Scenario-Conditioned Perceptual Assessment of Real-World User-Generated Content Images](https://mlsanity.com/paper/2610.17661/) (2026)

## Abstract (arXiv)

Computational inference of aesthetics is an ill-defined task due to its subjective nature. Many datasets have been proposed to tackle the problem by providing pairs of images and aesthetic scores based on human ratings. However, humans are better at expressing their opinion, taste, and emotions by means of language rather than summarizing them in a single number. In fact, photo critiques provide much richer information as they reveal how and why users rate the aesthetics of visual stimuli. In this regard, we propose the Reddit Photo Critique Dataset (RPCD), which contains tuples of image and photo critiques. RPCD consists of 74K images and 220K comments and is collected from a Reddit community used by hobbyists and professional photographers to improve their photography skills by leveraging constructive community feedback. The proposed dataset differs from previous aesthetics datasets mainly in three aspects, namely (i) the large scale of the dataset and the extension of the comments criticizing different aspects of the image, (ii) it contains mostly UltraHD images, and (iii) it can easily be extended to new data as it is collected through an automatic pipeline. To the best of our knowledge, in this work, we propose the first attempt to estimate the aesthetic quality of visual stimuli from the critiques. To this end, we exploit the polarity of the sentiment of criticism as an indicator of aesthetic judgment. We demonstrate how sentiment polarity correlates positively with the aesthetic judgment available for two aesthetic assessment benchmarks. Finally, we experiment with several models by using the sentiment scores as a target for ranking images. Dataset and baselines are available (https://github.com/mediatechnologycenter/aestheval).