# StarVQA: Space-Time Attention for Video Quality Assessment

arXiv: https://arxiv.org/abs/2108.09635 · HTML: https://mlsanity.com/paper/2108.09635/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 22 Aug 2021.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [Stand-Alone Self-Attention in Vision Models](https://mlsanity.com/paper/1906.05909/) (2019)
- [Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers](https://mlsanity.com/paper/2106.05392/) (2021)
- [Massive Online Crowdsourced Study of Subjective and Objective Picture Quality](https://mlsanity.com/paper/1511.02919/) (2015)
- [Quality Assessment of In-the-Wild Videos](https://mlsanity.com/paper/1908.00375/) (2019)
- [Patch-VQ: 'Patching Up' the Video Quality Problem](https://mlsanity.com/paper/2011.13544/) (2020)
- [UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content](https://mlsanity.com/paper/2005.14354/) (2020)
- [Large-Scale Study of Perceptual Video Quality](https://mlsanity.com/paper/1803.01761/) (2018)
- [RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content](https://mlsanity.com/paper/2101.10955/) (2021)
- [Space-time Mixing Attention for Video Transformer](https://mlsanity.com/paper/2106.05968/) (2021)
- [Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training](https://mlsanity.com/paper/2011.04263/) (2020)
- [Weeping and Gnashing of Teeth: Teaching Deep Learning in Image and Video Processing Classes](https://mlsanity.com/paper/2003.01870/) (2020)

## Built on by (most cited first, 15 of 15)

- [KVQ: Kwai Video Quality Assessment for Short-form Videos](https://mlsanity.com/paper/2402.07220/) (2024)
- [NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results](https://mlsanity.com/paper/2404.11313/) (2024)
- [Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment](https://mlsanity.com/paper/2308.00729/) (2023)
- [Enhancing Blind Video Quality Assessment with Rich Quality-aware Features](https://mlsanity.com/paper/2405.08745/) (2024)
- [Capturing Co-existing Distortions in User-Generated Content for No-reference Video Quality Assessment](https://mlsanity.com/paper/2307.16813/) (2023)
- [PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild](https://mlsanity.com/paper/2405.17765/) (2024)
- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks](https://mlsanity.com/paper/2309.13609/) (2023)
- [CLIPVQA:Video Quality Assessment via CLIP](https://mlsanity.com/paper/2407.04928/) (2024)
- [KVQ: Boosting Video Quality Assessment via Saliency-guided Local Perception](https://mlsanity.com/paper/2503.10259/) (2025)
- [HVS Revisited: A Comprehensive Video Quality Assessment Framework](https://mlsanity.com/paper/2210.04158/) (2022)
- [SKALD: Learning-Based Shot Assembly for Coherent Multi-Shot Video Creation](https://mlsanity.com/paper/2503.08010/) (2025)
- [Visual Mechanisms Inspired Efficient Transformers for Image and Video Quality Assessment](https://mlsanity.com/paper/2203.14557/) (2022)
- [FDIM: A Feature-distance-based Generic Video Quality Metric for Versatile Codecs](https://mlsanity.com/paper/2604.24123/) (2026)
- [Community-Aware Assessment of Social Textual Engagement and Resonance: A Human-Centric Perspective on User-Generated Content Evaluation](https://mlsanity.com/paper/2606.01897/) (2026)

## Abstract (arXiv)

The attention mechanism is blooming in computer vision nowadays. However, its application to video quality assessment (VQA) has not been reported. Evaluating the quality of in-the-wild videos is challenging due to the unknown of pristine reference and shooting distortion. This paper presents a novel underlinespace-underlinetime underlineattention network founderliner the underlineVQA problem, named StarVQA. StarVQA builds a Transformer by alternately concatenating the divided space-time attention. To adapt the Transformer architecture for training, StarVQA designs a vectorized regression loss by encoding the mean opinion score (MOS) to the probability vector and embedding a special vectorized label token as the learnable variable. To capture the long-range spatiotemporal dependencies of a video sequence, StarVQA encodes the space-time position information of each patch to the input of the Transformer. Various experiments are conducted on the de-facto in-the-wild video datasets, including LIVE-VQC, KoNViD-1k, LSVQ, and LSVQ-1080p. Experimental results demonstrate the superiority of the proposed StarVQA over the state-of-the-art. Code and model will be available at: https://github.com/DVL/StarVQA.