# An Image is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures

arXiv: https://arxiv.org/abs/1505.03358 · HTML: https://mlsanity.com/paper/1505.03358/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2015.

## Built on by (most cited first, 18 of 18)

- [Deep Neural Networks for Rank-Consistent Ordinal Regression Based On Conditional Probabilities](https://mlsanity.com/paper/2111.08851/) (2021)
- [Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression](https://mlsanity.com/paper/2103.13629/) (2021)
- [OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression](https://mlsanity.com/paper/2206.02338/) (2022)
- [Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification](https://mlsanity.com/paper/2306.13856/) (2023)
- [Teach CLIP to Develop a Number Sense for Ordinal Regression](https://mlsanity.com/paper/2408.03574/) (2024)
- [Ord2Seq: Regarding Ordinal Regression as Label Sequence Prediction](https://mlsanity.com/paper/2307.09004/) (2023)
- [Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment](https://mlsanity.com/paper/2206.08614/) (2022)
- [VisNumBench: Evaluating Number Sense of Multimodal Large Language Models](https://mlsanity.com/paper/2503.14939/) (2025)
- [Robust Image Ordinal Regression with Controllable Image Generation](https://mlsanity.com/paper/2305.04213/) (2023)
- [Ordinal-Aware Calibration for Ordinal Classification](https://mlsanity.com/paper/2410.15658/) (2024)
- [OrderChain: Towards General Instruct-Tuning for Stimulating the Ordinal Understanding Ability of MLLM](https://mlsanity.com/paper/2504.04801/) (2025)
- [Controlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learning](https://mlsanity.com/paper/2303.00396/) (2023)
- [Predicting beauty, liking, and aesthetic quality: A comparative analysis of image databases for visual aesthetics research](https://mlsanity.com/paper/2307.00984/) (2023)
- [Dual-level Fuzzy Learning with Patch Guidance for Image Ordinal Regression](https://mlsanity.com/paper/2505.05834/) (2025)
- [Style Similarity as Feedback for Product Design](https://mlsanity.com/paper/2105.12256/) (2021)
- [Detecting Cultural Differences in News Video Thumbnails via Computational Aesthetics](https://mlsanity.com/paper/2505.21912/) (2025)
- [D3O: Dynamic Distribution Distillation for Ordinal Regression](https://mlsanity.com/paper/2607.23575/) (2026)
- [Latent Ordinal Evidence, Misaligned Outputs: Inference-Time Ordinal Lens Alignment for Multimodal LLMs](https://mlsanity.com/paper/2608.20999/) (2026)

## Abstract (arXiv)

The dynamics of attention in social media tend to obey power laws. Attention concentrates on a relatively small number of popular items and neglecting the vast majority of content produced by the crowd. Although popularity can be an indication of the perceived value of an item within its community, previous research has hinted to the fact that popularity is distinct from intrinsic quality. As a result, content with low visibility but high quality lurks in the tail of the popularity distribution. This phenomenon can be particularly evident in the case of photo-sharing communities, where valuable photographers who are not highly engaged in online social interactions contribute with high-quality pictures that remain unseen. We propose to use a computer vision method to surface beautiful pictures from the immense pool of near-zero-popularity items, and we test it on a large dataset of creative-commons photos on Flickr. By gathering a large crowdsourced ground truth of aesthetics scores for Flickr images, we show that our method retrieves photos whose median perceived beauty score is equal to the most popular ones, and whose average is lower by only 1.5%.