# VideoSSL: Semi-Supervised Learning for Video Classification

arXiv: https://arxiv.org/abs/2003.00197 · HTML: https://mlsanity.com/paper/2003.00197/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2020.

## Built on by (most cited first, 27 of 27)

- [In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning](https://mlsanity.com/paper/2101.06329/) (2021)
- [SVFormer: Semi-supervised Video Transformer for Action Recognition](https://mlsanity.com/paper/2211.13222/) (2022)
- [Multiview Pseudo-Labeling for Semi-supervised Learning from Video](https://mlsanity.com/paper/2104.00682/) (2021)
- [Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition](https://mlsanity.com/paper/2112.09690/) (2021)
- [Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition](https://mlsanity.com/paper/2206.04790/) (2022)
- [Learning from Temporal Gradient for Semi-supervised Action Recognition](https://mlsanity.com/paper/2111.13241/) (2021)
- [Learning Representational Invariances for Data-Efficient Action Recognition](https://mlsanity.com/paper/2103.16565/) (2021)
- [Motion-Augmented Self-Training for Video Recognition at Smaller Scale](https://mlsanity.com/paper/2105.01646/) (2021)
- [TimeBalance: Temporally-Invariant and Temporally-Distinctive Video Representations for Semi-Supervised Action Recognition](https://mlsanity.com/paper/2303.16268/) (2023)
- [Important Object Identification with Semi-Supervised Learning for Autonomous Driving](https://mlsanity.com/paper/2203.02634/) (2022)
- [Cyclical Self-Supervision for Semi-Supervised Ejection Fraction Prediction from Echocardiogram Videos](https://mlsanity.com/paper/2210.11291/) (2022)
- [Language Semantic Graph Guided Data-Efficient Learning](https://mlsanity.com/paper/2311.08782/) (2023)
- [A Review of Machine Learning Methods Applied to Video Analysis Systems](https://mlsanity.com/paper/2312.05352/) (2023)
- [SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification](https://mlsanity.com/paper/2405.14506/) (2024)
- [FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition](https://mlsanity.com/paper/2409.01448/) (2024)
- [Semi-rPPG: Semi-Supervised Remote Physiological Measurement with Curriculum Pseudo-Labeling](https://mlsanity.com/paper/2502.03855/) (2025)
- [On the Surprising Effectiveness of Transformers in Low-Labeled Video Recognition](https://mlsanity.com/paper/2209.07474/) (2022)
- [ATCON: Attention Consistency for Vision Models](https://mlsanity.com/paper/2210.09705/) (2022)
- [Learning Discriminative Spatio-temporal Representations for Semi-supervised Action Recognition](https://mlsanity.com/paper/2404.16416/) (2024)
- [Knowledge-Spreader: Learning Facial Action Unit Dynamics with Extremely Limited Labels](https://mlsanity.com/paper/2203.16678/) (2022)
- [SITAR: Semi-supervised Image Transformer for Action Recognition](https://mlsanity.com/paper/2409.02910/) (2024)
- [Towards Micro-Action Recognition with Limited Annotations: An Asynchronous Pseudo Labeling and Training Approach](https://mlsanity.com/paper/2504.07785/) (2025)
- [Video-based Generalized Category Discovery via Memory-Guided Consistency-Aware Contrastive Learning](https://mlsanity.com/paper/2509.06306/) (2025)
- [How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbs](https://mlsanity.com/paper/2203.12344/) (2022)
- [Semi-Supervised Audio-Visual Video Action Recognition with Audio Source Localization Guided Mixup](https://mlsanity.com/paper/2503.02284/) (2025)
- [LEViL: Label-Efficient Video Learning via Zero-Shot Distillation over VLM-Generated Pseudo-Label Spaces](https://mlsanity.com/paper/2606.21358/) (2026)
- [Temporal Feature Distillation for Label-Efficient Precise Event Spotting in Sports Videos](https://mlsanity.com/paper/2607.10998/) (2026)

## Abstract (arXiv)

We propose a semi-supervised learning approach for video classification, VideoSSL, using convolutional neural networks (CNN). Like other computer vision tasks, existing supervised video classification methods demand a large amount of labeled data to attain good performance. However, annotation of a large dataset is expensive and time consuming. To minimize the dependence on a large annotated dataset, our proposed semi-supervised method trains from a small number of labeled examples and exploits two regulatory signals from unlabeled data. The first signal is the pseudo-labels of unlabeled examples computed from the confidences of the CNN being trained. The other is the normalized probabilities, as predicted by an image classifier CNN, that captures the information about appearances of the interesting objects in the video. We show that, under the supervision of these guiding signals from unlabeled examples, a video classification CNN can achieve impressive performances utilizing a small fraction of annotated examples on three publicly available datasets: UCF101, HMDB51 and Kinetics.