Open research questions in Anomaly Detection Techniques and Applications
180 unresolved questions extracted from the limitations and future-work sections of 462 Anomaly Detection Techniques and Applications papers in our library. Each links back to the study that raised it.
What the literature leaves open
Unstructured defects and highly dispersed intra-class semantics in industrial anomaly detection. Limited training samples for target categories in zero and few-shot scenarios. The need for a dedicated dataset for oil and gas pipeline scenarios.
Vision-guided multi-image prompt learning for zero and few-shot pipeline anomaly detection · 2026 · DOIExploring the application of ElainaCLIP to other industrial anomaly detection scenarios - Investigating the use of ElainaLoss and Multi-image Joint Optimization Strategy in other vision-language tasks
Vision-guided multi-image prompt learning for zero and few-shot pipeline anomaly detection · 2026 · DOIHigh data dimensionality and complex variable correlations in time series data. The need to balance temporal, variable, and frequency domain features for effective anomaly detection. The challenge of achieving accurate and efficient anomaly detection in real-world applications.
FCAE-Mixer: frequency-aware convolutional autoencoder with dual-branch mixing for time series anomaly detection · 2026 · DOIExisting methods often separate the synergistic mechanism among local temporal, global variable, and frequency domain features. Traditional methods based on statistical thresholds or single-feature modeling face challenges such as high data dimensionality and complex variable correlations.
FCAE-Mixer: frequency-aware convolutional autoencoder with dual-branch mixing for time series anomaly detection · 2026 · DOITemporal and representational discrepancies between RGB and Event data. Misaligned perception and diagnostic hallucinations of tiny defects in industrial scenarios. The need for efficient spatio-temporal alignment and information fusion of RGB-Event features.
Industrial anomaly detection via RGB-event fusion with multimodal large language models · 2026 · DOIFurther research on task-specific RGB-Event modeling and anomaly-oriented instruction tuning - Exploring the application of ReIAD-VL in other industrial scenarios - Investigating the use of ReIAD-VL in real-time anomaly monitoring and edge-based data processing
Industrial anomaly detection via RGB-event fusion with multimodal large language models · 2026 · DOIThe lack of a comprehensive overview of the latest techniques in deep learning for multivariate time series anomaly detection. The need for a taxonomy of anomaly detection strategies from the perspectives of learning paradigms and deep learning models.
A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions · 2025 · DOIMultiple long-term, medium-term, and short-term solutions have been proposed, - The study of the use and relevance of classifiers in the detection of violence
the lack of recent reviews dealing with the detection of violence in video. the need for improved violence detection systems, considering the infrequency of physical aggression compared to everyday activities.
In order to overcome the notable limitations of current methods for monitoring grain storage states, particularly in the early warning of potential risks and the analysis of the spatial distribution of grain temperatures within the granary, this study proposes a multi-model fusion approach based on a deep learning framework for grain storage state monitoring and risk alert.
Smart Grain Storage Solution: Integrated Deep Learning Framework for Grain Storage Monitoring and Risk Alert · 2025 · DOIAccurate real-time anomaly detection in dynamic process conditions. Reducing false alarms and improving yield and reliability. Developing a framework that is suitable for edge deployment with limited computing resources.
Memory-Efficient Artificial Intelligence Framework for Real-Time Multivariate Anomaly Detection · 2026 · DOITo evaluate the framework on larger datasets with more anomaly instances. To explore the application of the framework to other domains with similar challenges.
Memory-Efficient Artificial Intelligence Framework for Real-Time Multivariate Anomaly Detection · 2026 · DOITraditional anomaly detection paradigms are insufficient for forward-looking risk sensing in highly interconnected global capital markets. Prior research has focused on monolingual sentiment classification and keyword matching, limiting applicability in cross-lingual complex semantic environments. Existing strategies for combining textual and numerical data are shallow fusion approaches that lack interpretability.
A Cross-Modal Temporal Alignment Framework for Artificial Intelligence-Driven Sensing in Multilingual Risk Monitoring · 2026 · DOIThe results are naturally influenced by the specific dataset and system configuration considered in this study. Some datasets remain challenging, particularly those characterised by gradual or weak deviations from normal behaviour. Reconstruction error alone may be insufficient in some cases, and latent-space distances provide complementary information.
Topology-aware graph-attentive one-class anomaly detection for physics-based cybersecurity monitoring in photovoltaic systems · 2026 · DOIFuture extensions incorporating temporal context or explicit physics constraints could further enhance detection capability. The approach can be extended to other cyber-physical systems. Incorporating additional features or modalities could improve the performance of the proposed approach.
Topology-aware graph-attentive one-class anomaly detection for physics-based cybersecurity monitoring in photovoltaic systems · 2026 · DOIHigh dimensionality, nonlinearity, and nonstationarity of the data. Limited resources and energy efficiency in edge devices. The need for real-time data processing and machine learning at the edge.
Looking at anomaly detection in EdgeAI through the up-to-date lens of academic and market perspectives · 2026 · DOIIdentifying essential avenues for future research in EdgeAI anomaly detection. Developing algorithms for low-power environments. Exploring the applications of EdgeAI in various domains, such as robotics, autonomous machines, and IoT devices.
Looking at anomaly detection in EdgeAI through the up-to-date lens of academic and market perspectives · 2026 · DOIError accumulation and computational cost in incremental learning for anomaly detection in wind turbines. The need for a robust event-triggered IL strategy to address these concerns.
Anomaly Detection of Wind Turbines Based on MSET and Robust Event-Triggered Incremental Learning · 2026 · DOIFurther evaluation of the proposed framework using different datasets. Exploration of other deep learning architectures for crowd behavior and riot detection. Investigation of the use of other attention mechanisms in the BiGRU network.
The need for automated approaches to monitor crowded environments. The limitations of human operators supervising multiple video streams simultaneously. The lack of a robust framework for detecting abnormal crowd behavior and explicit security threats.
Future research should explore the development of AI-based cognitive systems for criminal profiling, including the use of machine learning models to integrate heterogeneous data into operational representations. Research should also investigate the application of AI in other domains, such as digital forensic psychology, to identify subtle behavioral changes and potential indicators of escalation or emerging risk.
The paper identifies a research gap in the application of AI in criminal profiling, particularly in the use of machine learning models to integrate heterogeneous data into operational representations. The gap is in the development of AI-based cognitive systems that can support investigative prioritization and reduce the effects of fatigue and information fragmentation.
Contamination of medical texts is a significant issue in clinical research and microbiology. Misidentification of cell lines, blood culture contamination, and errors in data entry or transcription can compromise data quality. The need for more effective and efficient methods for evaluating and improving medical data quality is a challenge.
Enhancing medical data quality using hybrid machine learning models: A comparative study of isolation forest and support vector machine on numerically encoded clinical text · 2026 · DOIThe study did not utilize deep learning techniques due to the need for interpretable, reproducible, and accessible quality assessment. The HIFSVM model may have limitations in terms of computational efficiency and scalability.
Enhancing medical data quality using hybrid machine learning models: A comparative study of isolation forest and support vector machine on numerically encoded clinical text · 2026 · DOIThe paper does not discuss the limitations of the proposed HS-COS framework in detail. The experimental evaluation is limited to a few real-world datasets. The paper does not provide a comprehensive comparison with other state-of-the-art outlier detection techniques.
Most-cited papers in Anomaly Detection Techniques and Applications
- Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median · Journal of Experimental Social Psychology · 2013 · 3,551 citations
- TranAD · Proceedings of the VLDB Endowment · 2022 · 1,020 citations
- Deep Isolation Forest for Anomaly Detection · IEEE Transactions on Knowledge and Data Engineering · 2023 · 522 citations
- Autoencoders and their applications in machine learning: a survey · Artificial Intelligence Review · 2024 · 511 citations
- Quo vadis artificial intelligence? · Discover Artificial Intelligence · 2022 · 482 citations
- Generalized Out-of-Distribution Detection: A Survey · International Journal of Computer Vision · 2024 · 441 citations
- BACON: blocked adaptive computationally efficient outlier nominators · Computational Statistics & Data Analysis · 2000 · 431 citations
- ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions · IEEE Transactions on Knowledge and Data Engineering · 2022 · 377 citations
- GAN-based anomaly detection: A review · Neurocomputing · 2022 · 375 citations
- Deep Industrial Image Anomaly Detection: A Survey · Machine Intelligence Research · 2024 · 348 citations
Most recent work
- Conditional outlier detection for clinical alerting · PubMed · 2026
- Gated Memory-Guided Multi-scale spatio–temporal–spectral feature fusion network for unsupervised Internet of Things time series anomaly detection · Engineering Applications of Artificial Intelligence · 2026
- Attention-driven pseudo-label self-training for weakly supervised video anomaly detection · Pattern Recognition · 2026
- An effluent risk informed closed-loop framework for early warning of influent anomalies using COD soft sensing · Water Research · 2026
- IPG-FRN: Intrinsic prototype-guided feature reconstruction network for industrial anomaly detection · Expert Systems with Applications · 2026
- A prototype correction multi-scale feature reconstruction network for industrial anomaly detection · Pattern Recognition · 2026
- Lightweight multimodal large language model enabling efficient one shot industrial visual anomaly detection · Discover Artificial Intelligence · 2026
- Memory-Efficient Artificial Intelligence Framework for Real-Time Multivariate Anomaly Detection · IEEE Internet of Things Journal · 2026
- HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series · Proceedings of the ACM on Management of Data · 2026
- The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis] · Proceedings of the ACM on Management of Data · 2026
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