CineScale2: a dataset of cinematic camera features in movies
Bibliographic record
Abstract
, are essential features in the film-making process due to their influence on the viewer's perception of the scene. We provide a database containing camera feature annotations on camera angle and camera level, for about 25,000 image frames. Frames are sampled from a wide range of movies, freely available images, and shots from cinematographic websites, and are annotated on the following five categories - Overhead, High, Neutral, Low, and Dutch - for what concerns camera angle, and on six different classes of camera level: Aerial, Eye, Shoulder, Hip, Knee, and Ground level. This dataset is an extension of the Cinescale dataset [1], which contains movie frames and related annotations regarding shot scale. The CineScale2 database enables AI-driven interpretation of shot scale data and opens to a large set of research activities related to the automatic visual analysis of cinematic material, such as movie stylistic analysis, video recommendation, and media psychology. To these purposes, we also provide the model and the code for building a Convolutional Neural Network (CNN) architecture for automated camera feature recognition. All the material is provided on the the project website; video frames can be also provided upon requests to authors, for research purposes under fair use.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.014 |
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".