# Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision

arXiv: https://arxiv.org/abs/2112.05181 · HTML: https://mlsanity.com/paper/2112.05181/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 68 references that are in the map.
- First posted on arXiv 9 Dec 2021.

## Builds on (most cited first, 30 of 58)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [Dense Contrastive Learning for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2011.09157/) (2020)
- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://mlsanity.com/paper/1705.08421/) (2017)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text](https://mlsanity.com/paper/2104.11178/) (2021)
- [Spatiotemporal Contrastive Video Representation Learning](https://mlsanity.com/paper/2008.03800/) (2020)
- [Video Action Transformer Network](https://mlsanity.com/paper/1812.02707/) (2018)

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

- [VideoPrism: A Foundational Visual Encoder for Video Understanding](https://mlsanity.com/paper/2402.13217/) (2024)
- [Video Transformers: A Survey](https://mlsanity.com/paper/2201.05991/) (2022)
- [Static and Dynamic Concepts for Self-supervised Video Representation Learning](https://mlsanity.com/paper/2207.12795/) (2022)
- [Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review](https://mlsanity.com/paper/2506.01364/) (2025)
- [Exploiting VLM Localizability and Semantics for Open Vocabulary Action Detection](https://mlsanity.com/paper/2411.10922/) (2024)
- [Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation Learning](https://mlsanity.com/paper/2309.00297/) (2023)
- [Multiscale Video Pretraining for Long-Term Activity Forecasting](https://mlsanity.com/paper/2307.12854/) (2023)
- [Face-to-Face Contrastive Learning for Social Intelligence Question-Answering](https://mlsanity.com/paper/2208.01036/) (2022)
- [MRI Contrast Enhancement Kinetics World Model](https://mlsanity.com/paper/2602.19285/) (2026)
- [Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models](https://mlsanity.com/paper/2510.26782/) (2025)
- [Spatio-Temporal Crop Aggregation for Video Representation Learning](https://mlsanity.com/paper/2211.17042/) (2022)
- [Similarity Contrastive Estimation for Image and Video Soft Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2212.11187/) (2022)
- [Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding](https://mlsanity.com/paper/2303.16341/) (2023)
- [KDC-MAE: Knowledge Distilled Contrastive Mask Auto-Encoder](https://mlsanity.com/paper/2411.12270/) (2024)
- [Toward Low-Latency Vision-Language Models with Doubly-Correct Predictions in Egocentric Visual Understanding](https://mlsanity.com/paper/2606.25160/) (2026)

## Abstract (arXiv)

Modern self-supervised learning algorithms typically enforce persistency of instance representations across views. While being very effective on learning holistic image and video representations, such an objective becomes sub-optimal for learning spatio-temporally fine-grained features in videos, where scenes and instances evolve through space and time. In this paper, we present Contextualized Spatio-Temporal Contrastive Learning (ConST-CL) to effectively learn spatio-temporally fine-grained video representations via self-supervision. We first design a region-based pretext task which requires the model to transform in-stance representations from one view to another, guided by context features. Further, we introduce a simple network design that successfully reconciles the simultaneous learning process of both holistic and local representations. We evaluate our learned representations on a variety of downstream tasks and show that ConST-CL achieves competitive results on 6 datasets, including Kinetics, UCF, HMDB, AVA-Kinetics, AVA and OTB.