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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2504.08012 |
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| _version_ | 1866912330517839872 |
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| author | Kim, Yuseon Park, Kyongseok |
| author_facet | Kim, Yuseon Park, Kyongseok |
| contents | Video prediction (VP) generates future frames by leveraging spatial representations and temporal context from past frames. Traditional recurrent neural network (RNN)-based models enhance memory cell structures to capture spatiotemporal states over extended durations but suffer from gradual loss of object appearance details. To address this issue, we propose the strong recollection VP (SRVP) model, which integrates standard attention (SA) and reinforced feature attention (RFA) modules. Both modules employ scaled dot-product attention to extract temporal context and spatial correlations, which are then fused to enhance spatiotemporal representations. Experiments on three benchmark datasets demonstrate that SRVP mitigates image quality degradation in RNN-based models while achieving predictive performance comparable to RNN-free architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08012 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SRVP: Strong Recollection Video Prediction Model Using Attention-Based Spatiotemporal Correlation Fusion Kim, Yuseon Park, Kyongseok Computer Vision and Pattern Recognition Video prediction (VP) generates future frames by leveraging spatial representations and temporal context from past frames. Traditional recurrent neural network (RNN)-based models enhance memory cell structures to capture spatiotemporal states over extended durations but suffer from gradual loss of object appearance details. To address this issue, we propose the strong recollection VP (SRVP) model, which integrates standard attention (SA) and reinforced feature attention (RFA) modules. Both modules employ scaled dot-product attention to extract temporal context and spatial correlations, which are then fused to enhance spatiotemporal representations. Experiments on three benchmark datasets demonstrate that SRVP mitigates image quality degradation in RNN-based models while achieving predictive performance comparable to RNN-free architectures. |
| title | SRVP: Strong Recollection Video Prediction Model Using Attention-Based Spatiotemporal Correlation Fusion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.08012 |