# You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution

arXiv: https://arxiv.org/abs/2207.06345 · HTML: https://mlsanity.com/paper/2207.06345/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 56 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 30 of 41)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://mlsanity.com/paper/1905.02716/) (2019)
- [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https://mlsanity.com/paper/2104.13371/) (2021)
- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond](https://mlsanity.com/paper/2012.02181/) (2020)
- [Softmax Splatting for Video Frame Interpolation](https://mlsanity.com/paper/2003.05534/) (2020)
- [Depth-Aware Video Frame Interpolation](https://mlsanity.com/paper/1904.00830/) (2019)
- [Video Frame Interpolation via Adaptive Separable Convolution](https://mlsanity.com/paper/1708.01692/) (2017)
- [Frame-Recurrent Video Super-Resolution](https://mlsanity.com/paper/1801.04590/) (2018)
- [Video Frame Interpolation via Adaptive Convolution](https://mlsanity.com/paper/1703.07514/) (2017)
- [Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation](https://mlsanity.com/paper/1611.05250/) (2016)
- [Context-aware Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/1803.10967/) (2018)
- [Detail-revealing Deep Video Super-resolution](https://mlsanity.com/paper/1704.02738/) (2017)
- [Recurrent Back-Projection Network for Video Super-Resolution](https://mlsanity.com/paper/1903.10128/) (2019)
- [AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation](https://mlsanity.com/paper/1907.10244/) (2019)
- [XVFI: eXtreme Video Frame Interpolation](https://mlsanity.com/paper/2103.16206/) (2021)
- [BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation](https://mlsanity.com/paper/2007.12622/) (2020)
- [Video Super-Resolution with Recurrent Structure-Detail Network](https://mlsanity.com/paper/2008.00455/) (2020)
- [MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement](https://mlsanity.com/paper/1810.08768/) (2018)
- [Asymmetric Bilateral Motion Estimation for Video Frame Interpolation](https://mlsanity.com/paper/2108.06815/) (2021)
- [Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution](https://mlsanity.com/paper/2002.11616/) (2020)
- [MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution](https://mlsanity.com/paper/2007.11803/) (2020)
- [Multiple Video Frame Interpolation via Enhanced Deformable Separable Convolution](https://mlsanity.com/paper/2006.08070/) (2020)
- [Temporal Modulation Network for Controllable Space-Time Video Super-Resolution](https://mlsanity.com/paper/2104.10642/) (2021)

## Built on by (most cited first, 3 of 3)

- [LAVIB: A Large-scale Video Interpolation Benchmark](https://mlsanity.com/paper/2406.09754/) (2024)
- [Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution](https://mlsanity.com/paper/2310.17294/) (2023)
- [NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing Framework](https://mlsanity.com/paper/2308.03121/) (2023)

## Abstract (arXiv)

Spatial-Temporal Video Super-Resolution (ST-VSR) technology generates high-quality videos with higher resolution and higher frame rates. Existing advanced methods accomplish ST-VSR tasks through the association of Spatial and Temporal video super-resolution (S-VSR and T-VSR). These methods require two alignments and fusions in S-VSR and T-VSR, which is obviously redundant and fails to sufficiently explore the information flow of consecutive spatial LR frames. Although bidirectional learning (future-to-past and past-to-future) was introduced to cover all input frames, the direct fusion of final predictions fails to sufficiently exploit intrinsic correlations of bidirectional motion learning and spatial information from all frames. We propose an effective yet efficient recurrent network with bidirectional interaction for ST-VSR, where only one alignment and fusion is needed. Specifically, it first performs backward inference from future to past, and then follows forward inference to super-resolve intermediate frames. The backward and forward inferences are assigned to learn structures and details to simplify the learning task with joint optimizations. Furthermore, a Hybrid Fusion Module (HFM) is designed to aggregate and distill information to refine spatial information and reconstruct high-quality video frames. Extensive experiments on two public datasets demonstrate that our method outperforms state-of-the-art methods in efficiency, and reduces calculation cost by about 22%.