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

2025-03-14

AI Summary

This paper addresses the challenge of reducing reliance on large-scale labeled data in text-image multimodal analysis through self-supervised contrastive learning. Methodologically, it systematically reviews cross-modal positive/negative sample construction, feature-space alignment mechanisms, and unsupervised representation learning paradigms. It introduces the first taxonomy of vision-language contrastive methods based on model architecture, integrating pretraining objectives, encoder designs (e.g., CLIP, ALIGN), and similarity measurement techniques into a unified analytical framework. The work clarifies the technical evolution trajectory and identifies core bottlenecks—including computational inefficiency, sensitivity to data distribution shifts, and limited interpretability. It further proposes a modeling pathway that jointly optimizes efficiency, robustness, and explainability. The contributions provide both theoretical foundations and practical guidelines for advancing self-supervised multimodal learning, enabling more scalable, generalizable, and transparent joint representation learning across modalities.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Explores self-supervised contrastive learning for text-image analysis.
• Categorizes approaches by model structures in text-image models.
• Reviews latest advances and applications in text-image contrastive learning.

Innovation

• Self-supervised learning for unlabeled data analysis
• Contrastive learning with positive and negative pairs
• Text-image model advancements and architectural structures