Category-Aware Fine-Tuning and Cross-Age Transferability inImage Memorability Prediction
Bibliographic record
Abstract
Image memorability is highly consistent across observers, yet current vision models achieve only moderate accuracy and remain below human consistency. We study two questions: (i) whether making semantic category structure explicit during training improves prediction, and (ii) whether adult-trained predictors transfer to adolescents, and whether any gains from category-specific adaptation generalize across observers of different age. We compare a mixed-category model (All) with per-category fine-tuning (CatFT) for two pretrained backbones, MemNet (AlexNet-based CNN) and ViT-B/16 (Vision Transformer), each fine-tuned on MemCat under All and CatFT. Adult-trained models are evaluated on Memoir (adolescent labels) without additional training to assess transfer, and Grad-CAM is used to examine which regions drive predictions on the best model. On adults, category-aware training increases Spearman’s rho for both backbones (ViT-B/16: 0.548→0.592; MemNet: 0.429→0.477). Memorability prediction itself transfers across age even without category-specific fine-tuning (ViT-B/16: rho=0.456 with All), with a small additional adolescent gain from CatFT (to rho=0.471); MemNet remains stable on adolescents (rho=0.405 with or without CatFT). Grad-CAM highlights semantically meaningful regions for highly memorable images and more diffuse patterns for low-memorability images. Overall, incorporating category structure improves adult accuracy, cross-age generalization of memorability prediction is robust, and among the tested backbones, ViT-B/16 performs best, with CatFT providing modest transfer gains.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".