# SRVP: Strong Recollection Video Prediction Model Using Attention-Based Spatiotemporal Correlation Fusion

arXiv: https://arxiv.org/abs/2504.08012 · HTML: https://mlsanity.com/paper/2504.08012/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Unsupervised Learning of Video Representations using LSTMs](https://mlsanity.com/paper/1502.04681/) (2015)
- [Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction](https://mlsanity.com/paper/2003.01460/) (2020)
- [SimVP: Simpler yet Better Video Prediction](https://mlsanity.com/paper/2206.05099/) (2022)
- [HRFormer: High-Resolution Transformer for Dense Prediction](https://mlsanity.com/paper/2110.09408/) (2021)
- [PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning](https://mlsanity.com/paper/1804.06300/) (2018)
- [Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics](https://mlsanity.com/paper/1811.07490/) (2018)
- [Space-time Mixing Attention for Video Transformer](https://mlsanity.com/paper/2106.05968/) (2021)
- [Convolutional Tensor-Train LSTM for Spatio-temporal Learning](https://mlsanity.com/paper/2002.09131/) (2020)
- [MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions](https://mlsanity.com/paper/2103.02243/) (2021)
- [Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization](https://mlsanity.com/paper/2103.04523/) (2021)
- [ConvTransformer: A Convolutional Transformer Network for Video Frame Synthesis](https://mlsanity.com/paper/2011.10185/) (2020)
- [MIMO Is All You Need : A Strong Multi-In-Multi-Out Baseline for Video Prediction](https://mlsanity.com/paper/2212.04655/) (2022)
- [Dual Prototype Attention for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2211.12036/) (2022)
- [Video Prediction by Efficient Transformers](https://mlsanity.com/paper/2212.06026/) (2022)
- [Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2309.05840/) (2023)

## Abstract (arXiv)

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.