Multi-Modal Anomaly Detection: A Survey
Multi-Modal Anomaly Detection (MMAD) is surveyed from an assumption-driven perspective rather than the traditional architecture-based classification, organizing methods into two paradigms: normality-assumption and anomaly-assumption approaches. Five intrinsic characteristics underlying core MMAD challenges are formally identified, providing a structured taxonomy for understanding the field's complexity. Normality-assumption methods model regularity through representation learning, cross-modal al
Analysis
TL;DR
- Multi-Modal Anomaly Detection (MMAD) is surveyed from an assumption-driven perspective rather than the traditional architecture-based classification, organizing methods into two paradigms: normality-assumption and anomaly-assumption approaches.
- Five intrinsic characteristics underlying core MMAD challenges are formally identified, providing a structured taxonomy for understanding the field's complexity.
- Normality-assumption methods model regularity through representation learning, cross-modal alignment, and knowledge enhancement, while anomaly-assumption methods sharpen decision boundaries via coarse-grained, structural, and semantic anomaly injection.
- Foundation models are reshaping MMAD through scalable pretraining, flexible cross-modal transfer, and emerging reasoning capabilities, opening new research directions.
- The survey compiles representative benchmarks and evaluation protocols across domains and highlights open problems for robust, adaptive, and interpretable MMAD systems.
Why It Matters
This survey provides a much-needed unifying framework for a fragmented field, offering researchers and practitioners a clear taxonomy to navigate the diverse MMAD literature spanning industrial inspection, cybersecurity, and other safety-critical domains. By organizing methods around how abnormality is defined rather than by architecture, it enables more principled method selection and comparison. The analysis of foundation model impacts is particularly timely as large-scale pretraining increasingly influences anomaly detection research.
Technical Details
- The paper formalizes the MMAD problem and identifies five intrinsic characteristics that underlie its core challenges, moving beyond superficial architectural groupings to a deeper understanding of what makes multi-modal anomaly detection fundamentally difficult.
- Two complementary paradigms are established: normality-assumption methods (modeling regularity via representation learning, cross-modal alignment, and knowledge enhancement) and anomaly-assumption methods (sharpening decision boundaries through coarse-grained, structural, and semantic anomaly injection).
- The survey examines how foundation models are transforming MMAD through three key mechanisms: scalable pretraining, flexible cross-modal transfer, and emerging reasoning capabilities, suggesting a shift toward more generalizable detection systems.
- Representative benchmarks and evaluation protocols across multiple domains are compiled, providing a practical reference for researchers designing and comparing MMAD systems.
Industry Insight
- Organizations deploying anomaly detection in safety-critical applications should evaluate whether normality-assumption or anomaly-assumption paradigms better suit their data characteristics and operational constraints, as each has distinct strengths in different scenarios.
- The integration of foundation models into MMAD pipelines represents a strategic opportunity to reduce domain-specific fine-tuning costs while improving cross-modal generalization, particularly for applications with limited labeled anomaly data.
- Researchers and practitioners should prioritize interpretability and adaptability in next-generation MMAD systems, as the survey identifies these as key open problems that will determine real-world deployability in high-stakes environments like industrial inspection and cybersecurity.
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