# Contrastive learning-based video quality assessment-jointed video vision transformer for video recognition

arXiv: https://arxiv.org/abs/2603.10965 · HTML: https://mlsanity.com/paper/2603.10965/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 55 references that are in the map.
- First posted on arXiv 11 Mar 2026.

## Builds on (most cited first, 19 of 19)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning](https://mlsanity.com/paper/2104.14558/) (2021)
- [Face Image Quality Assessment: A Literature Survey](https://mlsanity.com/paper/2009.01103/) (2020)
- [Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos](https://mlsanity.com/paper/2106.01111/) (2021)
- [Auto-Encoding Score Distribution Regression for Action Quality Assessment](https://mlsanity.com/paper/2111.11029/) (2021)
- [Video-based Facial Expression Recognition using Graph Convolutional Networks](https://mlsanity.com/paper/2010.13386/) (2020)
- [Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTM](https://mlsanity.com/paper/2102.10590/) (2021)
- [MC-ViViT: Multi-branch Classifier-ViViT to detect Mild Cognitive Impairment in older adults using facial videos](https://mlsanity.com/paper/2304.05292/) (2023)
- [GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content](https://mlsanity.com/paper/2305.02422/) (2023)
- [XnODR and XnIDR: Two Accurate and Fast Fully Connected Layers For Convolutional Neural Networks](https://mlsanity.com/paper/2111.10854/) (2021)
- [Multiview Contrastive Learning for Completely Blind Video Quality Assessment of User Generated Content](https://mlsanity.com/paper/2207.06148/) (2022)
- [Detection of Mild Cognitive Impairment Using Facial Features in Video Conversations](https://mlsanity.com/paper/2308.15624/) (2023)
- [A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection](https://mlsanity.com/paper/2410.19898/) (2024)
- [Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN](https://mlsanity.com/paper/2402.02262/) (2024)
- [SE-GCL: An Event-Based Simple and Effective Graph Contrastive Learning for Text Representation](https://mlsanity.com/paper/2412.11652/) (2024)

## Abstract (arXiv)

Video quality significantly affects video classification. We found this problem when we classified Mild Cognitive Impairment well from clear videos, but worse from blurred ones. From then, we realized that referring to Video Quality Assessment (VQA) may improve video classification. This paper proposed Self-Supervised Learning-based Video Vision Transformer combined with No-reference VQA for video classification (SSL-V3) to fulfill the goal. SSL-V3 leverages Combined-SSL mechanism to join VQA into video classification and address the label shortage of VQA, which commonly occurs in video datasets, making it impossible to provide an accurate Video Quality Score. In brief, Combined-SSL takes video quality score as a factor to directly tune the feature map of the video classification. Then, the score, as an intersected point, links VQA and classification, using the supervised classification task to tune the parameters of VQA. SSL-V3 achieved robust experimental results on two datasets. For example, it reached an accuracy of 94.87% on some interview videos in the I-CONECT (a facial video-involved healthcare dataset), verifying SSL-V3's effectiveness.