Improving workflows in digital art history: sharing annotations for patrimonial images segmentation and object detection
Bibliographic record
Abstract
[english] This article addresses the lack of standardization in digital art history, with a particular focus on image segmentation and object detection. It argues for the establishment of interoperable and reproducible data standards to enhance the effectiveness and replicability of current workflows. Through the case study of animal representations in ancient India, the article underscores the necessity of harmonizing segmentation and object detection practices, while also recognizing the interpretive nature of art historical analysis. It advocates for the development of more adaptable tools that can accommodate the diverse levels of interpretation inherent in artworks, facilitating the integration of multi-layered semantic annotations. [français] Cet article aborde l’absence de standardisation dans le domaine de l’histoire de l’art numérique, en mettant particulièrement l’accent sur la segmentation d’images et la détection d’objets. Il plaide en faveur de l’établissement de standards de données interopérables et reproductibles afin d’améliorer l’efficacité et la reproductibilité des flux de travail actuels. À travers l’étude de cas des représentations animales dans les sculptures produites en Inde ancienne, l’article souligne la nécessité d’harmoniser les pratiques de segmentation et de détection d’objets, tout en reconnaissant le caractère interprétatif de l’analyse en histoire de l’art. Il préconise le développement d’outils plus flexibles, capables d’intégrer les différents niveaux d’interprétation propres aux œuvres d'art, et de faciliter l’intégration d’annotations sémantiques multi-niveaux.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".