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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2501.16997 |
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| _version_ | 1866914427971829760 |
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| author | Gupta, Shreyam Agrawal, P. Gupta, Priyam |
| author_facet | Gupta, Shreyam Agrawal, P. Gupta, Priyam |
| contents | The fast progress in computer vision has necessitated more advanced methods for temporal sequence modeling. This area is essential for the operation of autonomous systems, real-time surveillance, and predicting anomalies. As the demand for accurate video prediction increases, the limitations of traditional deterministic models, particularly their struggle to maintain long-term temporal coherence while providing high-frequency spatial detail, have become very clear. This report provides an exhaustive analysis of the Multi-Attention Unit Cell (MAUCell), a novel architectural framework that represents a significant leap forward in video frame prediction. By synergizing Generative Adversarial Networks (GANs) with a hierarchical "STAR-GAN" processing strategy and a triad of specialized attention mechanisms (Temporal, Spatial, and Pixel-wise), the MAUCell addresses the persistent "deep-in-time" dilemma that plagues Recurrent Neural Networks (RNNs). Our analysis shows that the MAUCell framework successfully establishes a new state-of-the-art benchmark, especially in its ability to produce realistic video sequences that closely resemble real-world footage while ensuring efficient inference for real-time deployment. Through rigorous evaluation on datasets: Moving MNIST, KTH Action, and CASIA-B, the framework shows superior performance metrics, especially in Learned Perceptual Image Patch Similarity (LPIPS) and Structural Similarity Index (SSIM). This success confirms its dual-pathway information transformation system. This report details the theoretical foundations, detailed structure and broader significance of MAUCell, presenting it as a valuable solution for video forecasting tasks that require high precision and limited resources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16997 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Resolving Spatio-Temporal Entanglement in Video Prediction via Multi-Modal Attention Gupta, Shreyam Agrawal, P. Gupta, Priyam Computer Vision and Pattern Recognition Machine Learning Robotics The fast progress in computer vision has necessitated more advanced methods for temporal sequence modeling. This area is essential for the operation of autonomous systems, real-time surveillance, and predicting anomalies. As the demand for accurate video prediction increases, the limitations of traditional deterministic models, particularly their struggle to maintain long-term temporal coherence while providing high-frequency spatial detail, have become very clear. This report provides an exhaustive analysis of the Multi-Attention Unit Cell (MAUCell), a novel architectural framework that represents a significant leap forward in video frame prediction. By synergizing Generative Adversarial Networks (GANs) with a hierarchical "STAR-GAN" processing strategy and a triad of specialized attention mechanisms (Temporal, Spatial, and Pixel-wise), the MAUCell addresses the persistent "deep-in-time" dilemma that plagues Recurrent Neural Networks (RNNs). Our analysis shows that the MAUCell framework successfully establishes a new state-of-the-art benchmark, especially in its ability to produce realistic video sequences that closely resemble real-world footage while ensuring efficient inference for real-time deployment. Through rigorous evaluation on datasets: Moving MNIST, KTH Action, and CASIA-B, the framework shows superior performance metrics, especially in Learned Perceptual Image Patch Similarity (LPIPS) and Structural Similarity Index (SSIM). This success confirms its dual-pathway information transformation system. This report details the theoretical foundations, detailed structure and broader significance of MAUCell, presenting it as a valuable solution for video forecasting tasks that require high precision and limited resources. |
| title | Resolving Spatio-Temporal Entanglement in Video Prediction via Multi-Modal Attention |
| topic | Computer Vision and Pattern Recognition Machine Learning Robotics |
| url | https://arxiv.org/abs/2501.16997 |