The exponential growth in data availability, driven by advancements in sensing, storage, and communication technologies, has transformed numerous sectors, including healthcare, industry, finance, and scientific research. As data becomes increasingly diverse in form and origin, ranging from text and images to audio and video, the challenge of integrating and interpreting this heterogeneous information has become more critical than ever.
The quality of the representations learned from data is the key to many successful machine learning and artificial intelligence (AI) applications. The effectiveness of downstream tasks such as classification, retrieval, recommendation, and generation is strongly influenced by how input information is encoded and the methods used to compare and relate different data instances.
This has led to growing interest in representation learning, particularly regarding strategies beyond traditional single-modality approaches. Multimodal representation learning aims to bridge distinct data types, enabling models to process and relate information across different modalities. Graph-based learning provides powerful mechanisms for modeling relational structures and dependencies. Conversely, contrastive learning, both supervised and self-supervised, has demonstrated strong potential in capturing discriminative and transferable representations, even in scenarios with limited data or noisy labels.
In this context, new paradigms are emerging that combine these approaches to create more expressive, adaptable, and trustworthy models.
This Research Topic aims to advance the field of representation learning by investigating emerging approaches that integrate multimodal, contrastive learning, or graph-based strategies. Specifically, it aims to:
- Promote the development of models capable of learning expressive, discriminative, and transferable representations from heterogeneous and complex data; - Encourage novel approaches that leverage cross-modal fusion, graph-structured information, and contrastive learning to enhance performance in challenging real-world scenarios; - Foster research on trustworthy, interpretable, and fair representation learning techniques that address robustness, explainability, and ethical concerns; - Support the application of these methods in diverse domains such as healthcare, robotics, autonomous systems, and privacy-preserving machine learning.
Through this Research Topic, we seek contributions that bridge foundational advances with practical implementations, enabling robust and effective learning across modalities and tasks.
We invite contributions that address, but are not limited to:
● Multimodal Representation Learning ○ Cross-modal alignment and fusion strategies (e.g., vision-language, audio-text) ○ Self-supervised learning for multimodal data ○ Zero-shot and few-shot learning across modalities
● Graph-based Representation Learning ○ Advances in Graph Neural Networks (GNNs) ○ Dynamic and temporal graph representation learning ○ Graph transformers and attention-based methods ○ Applications of graph learning ○ Explainability and interpretability in GNNs ○ Graph-based contrastive learning techniques
● Contrastive Representation Learning ○ Supervised and self-supervised contrastive representation learning ○ Data mining for representation learning ○ Contrastive approaches for multimodal and graph data ○ Contrastive learning under noisy and incomplete labels
● Trustworthy & Interpretable Representation Learning ○ Model interpretability and post-hoc explanation methods ○ Fairness, bias mitigation, and debiasing techniques ○ Robustness to adversarial examples and distribution shifts
● Integration and Applications ○ Representation learning for downstream tasks (e.g., classification, retrieval, generation, segmentation). ○ Representation learning for real-world applications: healthcare, robotics, autonomous systems, etc. ○ Federated and privacy-preserving representation learning
Topic Editors Lucas Pascotti Valem, Mirela Teixeira Cazzolato, and Daniel Carlos Guimarães Pedronette received financial support from Petrobras. Topic Editor Vinitra Swamy is the founder and CEO of Scholé AI. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Clinical Trial
Community Case Study
Conceptual Analysis
Data Report
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
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Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
