# A Survey on Self-supervised Contrastive Learning for Multimodal Text-Image Analysis

arXiv: https://arxiv.org/abs/2503.11101 · HTML: https://mlsanity.com/paper/2503.11101/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data](https://mlsanity.com/paper/1908.04616/) (2019)
- [SLIP: Self-supervision meets Language-Image Pre-training](https://mlsanity.com/paper/2112.12750/) (2021)
- [Contrastive learning, multi-view redundancy, and linear models](https://mlsanity.com/paper/2008.10150/) (2020)
- [LP++: A Surprisingly Strong Linear Probe for Few-Shot CLIP](https://mlsanity.com/paper/2404.02285/) (2024)
- [Retrieval-Enhanced Contrastive Vision-Text Models](https://mlsanity.com/paper/2306.07196/) (2023)
- [A Recent Survey of Vision Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2312.00634/) (2023)
- [Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training](https://mlsanity.com/paper/2311.01734/) (2023)

## Built on by (most cited first, 5 of 5)

- [AutoGen Driven Multi Agent Framework for Iterative Crime Data Analysis and Prediction](https://mlsanity.com/paper/2506.11475/) (2025)
- [Advances in LLMs with Focus on Reasoning, Adaptability, Efficiency and Ethics](https://mlsanity.com/paper/2506.12365/) (2025)
- [DreamSAC: Learning Hamiltonian World Models via Symmetry Exploration](https://mlsanity.com/paper/2603.07545/) (2026)
- [Understanding InfoNCE: Transition Probability Matrix Induced Feature Clustering](https://mlsanity.com/paper/2511.12180/) (2025)
- [Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts](https://mlsanity.com/paper/2605.03348/) (2026)

## Abstract (arXiv)

Self-supervised learning is a machine learning approach that generates implicit labels by learning underlined patterns and extracting discriminative features from unlabeled data without manual labelling. Contrastive learning introduces the concept of "positive" and "negative" samples, where positive pairs (e.g., variation of the same image/object) are brought together in the embedding space, and negative pairs (e.g., views from different images/objects) are pushed farther away. This methodology has shown significant improvements in image understanding and image text analysis without much reliance on labeled data. In this paper, we comprehensively discuss the terminologies, recent developments and applications of contrastive learning with respect to text-image models. Specifically, we provide an overview of the approaches of contrastive learning in text-image models in recent years. Secondly, we categorize the approaches based on different model structures. Thirdly, we further introduce and discuss the latest advances of the techniques used in the process such as pretext tasks for both images and text, architectural structures, and key trends. Lastly, we discuss the recent state-of-art applications of self-supervised contrastive learning Text-Image based models.