GenVis: Visualizing Genre Detection in Movie Trailers for Enhanced Understanding
Bibliographic record
Abstract
Automatic movie genre detection is vital for improving content recommendations, user experiences, and organization. Multi-label generation detection assigns multiple labels to a movie and recognizes a movie’s diverse themes. Although there are many existing methods for generating multiple genre labels from movies but do not provide comprehensive analysis and visual depiction. This work introduces GenVis, a visualization system that provides a better understanding of multi-label genres extracted from movie trailers. The system initially uses text and visual features to classify trailers and assign multiple genre labels and probabilities. Next, GenVis provides four visualization views: a video view for trailer observation, an overall genre view for getting insights into genre distribution, a genre timeline view for temporal genre evolution, and finally, a genre flow summary for more focused genre analysis. The system allows users to pause the frames, sort the results, and process multiple videos. The multi-label classification is rigorously evaluated using MSE, cross-entropy loss, precision, recall, F1-score metrics, achieving high accuracy, and demonstrating strong genre correlations with notable precision in effectively classifying and distinguishing movie genres. Additionally, a user evaluation for visualization evaluation demonstrated the effectiveness and intuitive usability of GenVis with a high overall rating of 4.25 out of 5.0.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".