This paper presents a comprehensive survey on heterogeneous contrastive learning for foundation models, focusing on both view and task heterogeneity. The authors review recent advances in contrastive learning (CL) for training multi-view foundation models, discuss CL methods for task heterogeneity, including pre-training and downstream tasks, and highlight open challenges and future directions in contrastive learning. The paper emphasizes the importance of CL in learning compact and high-quality representations without relying on labeled data, and its application in various domains such as computer vision, natural language processing, and graph learning. It also addresses the challenges of applying CL to large-scale heterogeneous data, including computational efficiency, representation redundancy, and the need for high-quality benchmark datasets. The authors conclude that future research should focus on improving the efficiency of CL-based foundation models, developing better multi-view benchmark datasets, ensuring trustworthy and interpretable CL models, and understanding the mechanisms between CL strategies and downstream tasks. The survey provides a systematic review of existing methods and identifies key areas for further exploration in the field of heterogeneous contrastive learning.This paper presents a comprehensive survey on heterogeneous contrastive learning for foundation models, focusing on both view and task heterogeneity. The authors review recent advances in contrastive learning (CL) for training multi-view foundation models, discuss CL methods for task heterogeneity, including pre-training and downstream tasks, and highlight open challenges and future directions in contrastive learning. The paper emphasizes the importance of CL in learning compact and high-quality representations without relying on labeled data, and its application in various domains such as computer vision, natural language processing, and graph learning. It also addresses the challenges of applying CL to large-scale heterogeneous data, including computational efficiency, representation redundancy, and the need for high-quality benchmark datasets. The authors conclude that future research should focus on improving the efficiency of CL-based foundation models, developing better multi-view benchmark datasets, ensuring trustworthy and interpretable CL models, and understanding the mechanisms between CL strategies and downstream tasks. The survey provides a systematic review of existing methods and identifies key areas for further exploration in the field of heterogeneous contrastive learning.