Trailers12k: Improving Transfer Learning with a Dual Image and Video Transformer for Multi-Label Movie Trailer Genre Classification

Montalvo-Lezama, Ricardo; Montalvo-Lezama, Berenice; Fuentes-Pineda, Gibrán

doi:10.2139/ssrn.4253487

Cited by 2 publications

(1 citation statement)

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“…The implementation of convolutional neural networks (CNNs) in computer vision has led to significant progress [14]. Despite the considerable research dedicated to this subject, the categorization of films remains complex and requires further examination [15].…”

Section: Introductionmentioning

confidence: 99%

Enhancing multimedia management: cloud-based movie type recognition with hybrid deep learning architecture

Lin,

Yuan,

Chen

et al. 2024

J Cloud Comp

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Film and movie genres play a pivotal role in captivating relevant audiences across interactive multimedia platforms. With a focus on entertainment, streaming providers are increasingly prioritizing the automatic generation of movie genres within cloud-based media services. In service management, the integration of a hybrid convolutional network proves to be instrumental in effectively distinguishing between a diverse array of video genres. This classification process not only facilitates more refined recommendations and content filtering but also enables targeted advertising. Furthermore, given the frequent amalgamation of components from various genres in cinema, there arises a need for social media networks to incorporate real-time video classification mechanisms for accurate genre identification. In this study, we propose a novel architecture leveraging deep learning techniques for the detection and classification of genres in video films. Our approach entails the utilization of a bidirectional long- and short-term memory (BiLSTM) network, augmented with video descriptors extracted from EfficientNet-B7, an ImageNet pre-trained convolutional neural network (CNN) model. By employing BiLSTM, the network acquires robust video representations and proficiently categorizes movies into multiple genres. Evaluation on the LMTD dataset demonstrates the substantial improvement in the performance of the movie genre classifier system achieved by our proposed architecture. Notably, our approach achieves both computational efficiency and precision, outperforming even the most sophisticated models. Experimental results reveal that EfficientNet-BiLSTM achieves a precision rate of 93.5%. Furthermore, our proposed architecture attains state-of-the-art performance, as evidenced by its F1 score of 0.9012.

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Section: Introductionmentioning

confidence: 99%