# Video Super-resolution with Temporal Group Attention

arXiv: https://arxiv.org/abs/2007.10595 · HTML: https://mlsanity.com/paper/2007.10595/

## Key facts

- Cited by 81 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 30 of 81)

- [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https://mlsanity.com/paper/2104.13371/) (2021)
- [VRT: A Video Restoration Transformer](https://mlsanity.com/paper/2201.12288/) (2022)
- [Recurrent Video Restoration Transformer with Guided Deformable Attention](https://mlsanity.com/paper/2206.02146/) (2022)
- [Investigating Tradeoffs in Real-World Video Super-Resolution](https://mlsanity.com/paper/2111.12704/) (2021)
- [PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2103.10596/) (2021)
- [Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution](https://mlsanity.com/paper/2312.06640/) (2023)
- [Video Super-Resolution Transformer](https://mlsanity.com/paper/2106.06847/) (2021)
- [VEnhancer: Generative Space-Time Enhancement for Video Generation](https://mlsanity.com/paper/2407.07667/) (2024)
- [Learning Trajectory-Aware Transformer for Video Super-Resolution](https://mlsanity.com/paper/2204.04216/) (2022)
- [TAPE: Task-Agnostic Prior Embedding for Image Restoration](https://mlsanity.com/paper/2203.06074/) (2022)
- [Multi-Target Domain Adaptation with Collaborative Consistency Learning](https://mlsanity.com/paper/2106.03418/) (2021)
- [Low-Light Video Enhancement with Synthetic Event Guidance](https://mlsanity.com/paper/2208.11014/) (2022)
- [Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution](https://mlsanity.com/paper/2303.13767/) (2023)
- [Omniscient Video Super-Resolution](https://mlsanity.com/paper/2103.15683/) (2021)
- [Mitigating Artifacts in Real-World Video Super-Resolution Models](https://mlsanity.com/paper/2212.07339/) (2022)
- [Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention](https://mlsanity.com/paper/2401.06312/) (2024)
- [FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring](https://mlsanity.com/paper/2401.03707/) (2024)
- [Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution](https://mlsanity.com/paper/2403.17000/) (2024)
- [Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling](https://mlsanity.com/paper/2204.07114/) (2022)
- [FDAN: Flow-guided Deformable Alignment Network for Video Super-Resolution](https://mlsanity.com/paper/2105.05640/) (2021)
- [Self-Conditioned Probabilistic Learning of Video Rescaling](https://mlsanity.com/paper/2107.11639/) (2021)
- [Local-Global Temporal Difference Learning for Satellite Video Super-Resolution](https://mlsanity.com/paper/2304.04421/) (2023)
- [Learning for Unconstrained Space-Time Video Super-Resolution](https://mlsanity.com/paper/2102.13011/) (2021)
- [Memory-Augmented Non-Local Attention for Video Super-Resolution](https://mlsanity.com/paper/2108.11048/) (2021)
- [Temporal Consistency Learning of inter-frames for Video Super-Resolution](https://mlsanity.com/paper/2211.01639/) (2022)
- [NTIRE 2021 Challenge on Video Super-Resolution](https://mlsanity.com/paper/2104.14852/) (2021)
- [Self-Supervised Deep Blind Video Super-Resolution](https://mlsanity.com/paper/2201.07422/) (2022)
- [Structured Sparsity Learning for Efficient Video Super-Resolution](https://mlsanity.com/paper/2206.07687/) (2022)
- [Learning Truncated Causal History Model for Video Restoration](https://mlsanity.com/paper/2410.03936/) (2024)
- [Show and Polish: Reference-Guided Identity Preservation in Face Video Restoration](https://mlsanity.com/paper/2507.10293/) (2025)

## Abstract (arXiv)

Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, has recently drawn increasing attention. In this work, we propose a novel method that can effectively incorporate temporal information in a hierarchical way. The input sequence is divided into several groups, with each one corresponding to a kind of frame rate. These groups provide complementary information to recover missing details in the reference frame, which is further integrated with an attention module and a deep intra-group fusion module. In addition, a fast spatial alignment is proposed to handle videos with large motion. Extensive results demonstrate the capability of the proposed model in handling videos with various motion. It achieves favorable performance against state-of-the-art methods on several benchmark datasets.