Video-Based MPAA Rating Prediction: An Attention-Driven Hybrid Architecture Using Contrastive Learning

• URL: http://arxiv.org/abs/2509.06826v1
• Date: Mon, 08 Sep 2025 16:01:02 GMT
• Title: Video-Based MPAA Rating Prediction: An Attention-Driven Hybrid Architecture Using Contrastive Learning
• Authors: Dipta Neogi, Nourash Azmine Chowdhury, Muhammad Rafsan Kabir, Mohammad Ashrafuzzaman Khan,
• Abstract summary: We employ contrastive learning for improved discrimination and adaptability, exploring three frameworks: Instance Discrimination, Contextual Contrastive Learning, and Multi-View Contrastive Learning.<n>Our hybrid architecture integrates an LRCN (CNN+LSTM) backbone with a Bahdanau attention mechanism, achieving state-of-the-art performance in the Contextual Contrastive Learning framework, with 88% accuracy and an F1 score of 0.8815.<n>We evaluate the model's performance across various contrastive loss functions, including NT-Xent, NT-logistic, and Triple Margint, demonstrating the robustness of our proposed architecture.
• Score: 0.1749935196721634
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid growth of visual content consumption across platforms necessitates automated video classification for age-suitability standards like the MPAA rating system (G, PG, PG-13, R). Traditional methods struggle with large labeled data requirements, poor generalization, and inefficient feature learning. To address these challenges, we employ contrastive learning for improved discrimination and adaptability, exploring three frameworks: Instance Discrimination, Contextual Contrastive Learning, and Multi-View Contrastive Learning. Our hybrid architecture integrates an LRCN (CNN+LSTM) backbone with a Bahdanau attention mechanism, achieving state-of-the-art performance in the Contextual Contrastive Learning framework, with 88% accuracy and an F1 score of 0.8815. By combining CNNs for spatial features, LSTMs for temporal modeling, and attention mechanisms for dynamic frame prioritization, the model excels in fine-grained borderline distinctions, such as differentiating PG-13 and R-rated content. We evaluate the model's performance across various contrastive loss functions, including NT-Xent, NT-logistic, and Margin Triplet, demonstrating the robustness of our proposed architecture. To ensure practical application, the model is deployed as a web application for real-time MPAA rating classification, offering an efficient solution for automated content compliance across streaming platforms.

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