Improving Transfer Learning for Movie Trailer Genre Classification using a Dual Image and Video Transformer

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

doi:10.1016/j.ipm.2023.103343

Cited by 9 publications

(2 citation statements)

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“…Video-based movie classification has several applications such as genre-based video retrieval and filtering [5], automated labeling and annotation [6], and content recommendation [7,8]. Early research focused on predicting film genres from input movies using domain-specific datasets as the primary aim [9][10][11]. Methods sometimes involve adapting models from existing video classification tasks related to the specific challenge, such as action identification or theme recognition.…”

Section: Introductionmentioning

confidence: 99%

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Recognizing online video genres using ensemble deep convolutional learning for digital media service management

Shao,

Guo

2024

J Cloud Comp

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It's evident that streaming services increasingly seek to automate the generation of film genres, a factor profoundly shaping a film's structure and target audience. Integrating a hybrid convolutional network into service management emerges as a valuable technique for discerning various video formats. This innovative approach not only categorizes video content but also facilitates personalized recommendations, content filtering, and targeted advertising. Given the tendency of films to blend elements from multiple genres, there is a growing demand for a real-time video classification system integrated with social media networks. Leveraging deep learning, we introduce a novel architecture for identifying and categorizing video film genres. Our approach utilizes an ensemble gated recurrent unit (ensGRU) neural network, effectively analyzing motion, spatial information, and temporal relationships. Additionally,w we present a sophisticated deep neural network incorporating the recommended GRU for video genre classification. The adoption of a dual-model strategy allows the network to capture robust video representations, leading to exceptional performance in multi-class movie classification. Evaluations conducted on well-known datasets, such as the LMTD dataset, consistently demonstrate the high performance of the proposed GRU model. This integrated model effectively extracts and learns features related to motion, spatial location, and temporal dynamics. Furthermore, the effectiveness of the proposed technique is validated using an engine block assembly dataset. Following the implementation of the enhanced architecture, the movie genre categorization system exhibits substantial improvements on the LMTD dataset, outperforming advanced models while requiring less computing power. With an impressive F1 score of 0.9102 and an accuracy rate of 94.4%, the recommended model consistently delivers outstanding results. Comparative evaluations underscore the accuracy and effectiveness of our proposed model in accurately identifying and classifying video genres, effectively extracting contextual information from video descriptors. Additionally, by integrating edge processing capabilities, our system achieves optimal real-time video processing and analysis, further enhancing its performance and relevance in dynamic media environments.

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Section: Introductionmentioning

confidence: 99%

“…Challenges exist when implementing video classification systems for predicting movie genres. Prior research has typically overlooked linguistic cues in videos that might provide genre information [9,10,[16][17][18]. Movie transcripts can be used to produce accurate predictions in some cases [19].…”

Section: Introductionmentioning

confidence: 99%