{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T08:16:32Z","timestamp":1763885792895,"version":"3.45.0"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI-SS"],"abstract":"<jats:p>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\u2019s rho for both backbones (ViT-B\/16: 0.548\u21920.592; MemNet: 0.429\u21920.477). 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.<\/jats:p>","DOI":"10.1609\/aaaiss.v7i1.36919","type":"journal-article","created":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T08:15:04Z","timestamp":1763885704000},"page":"466-473","source":"Crossref","is-referenced-by-count":0,"title":["Category-Aware Fine-Tuning and Cross-Age Transferability in\nImage Memorability Prediction"],"prefix":"10.1609","volume":"7","author":[{"given":"Elham","family":"Bagheri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johann","family":"Cardenas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yalda","family":"Mohsenzadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2025,11,23]]},"container-title":["Proceedings of the AAAI Symposium Series"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/36919\/39057","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/36919\/39057","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T08:15:05Z","timestamp":1763885705000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/view\/36919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,23]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,11,23]]}},"URL":"https:\/\/doi.org\/10.1609\/aaaiss.v7i1.36919","relation":{},"ISSN":["2994-4317"],"issn-type":[{"value":"2994-4317","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,23]]}}}