Bibliographic record
Abstract
Objective image aesthetics assessment (IAA) is attracting an increasing amount of attention in recent years. One of the most critical issues that hampers IAA research is the lack of publicly available and reliable image databases that can be used to train and test IAA features and models, especially those databases that offer continuous-valued subjective opinion scores. In this work, we construct a Waterloo IAA database containing more than 1,000 images, and carry out a lab-controlled subjective user study. There are several unique and desirable features of the new database as compared to existing ones - It helps us better understand the level of diversity of subject opinions; it provides continuous-valued IAA scores approximately evenly distributed from poor to excellent aesthetics levels; it also allows us to test the effectiveness of various aesthetics features on predicting continuous aesthetics scores. Using the new database as a benchmark, we test more than 1,000 IAA features. The results indicate that existing features are still weak at aesthetics estimation, and the effectiveness of aesthetics features are content dependent. Therefore, understanding and assessing image aesthetics remain a major challenge for future research. The database will be made publicly available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".