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[2508.11936] M3OOD: Automatic Selection of Multimodal OOD Detectors
Summary
The paper presents M3OOD, a meta-learning framework designed for the automatic selection of out-of-distribution (OOD) detectors in multimodal machine learning settings, demonstrating improved performance across various scenarios.
Why It Matters
As machine learning systems increasingly handle diverse data types, the ability to effectively select OOD detectors is crucial for ensuring robustness. M3OOD addresses the challenge of model selection in dynamic environments, potentially enhancing the reliability of AI applications in real-world scenarios.
Key Takeaways
- M3OOD utilizes meta-learning to adaptively select OOD detectors.
- The framework combines multimodal embeddings with meta-features for improved performance.
- Experimental results show M3OOD outperforms 10 existing baselines.
- The approach minimizes computational overhead while maximizing detection accuracy.
- M3OOD is applicable across various multimodal benchmarks.
Computer Science > Machine Learning arXiv:2508.11936 (cs) [Submitted on 16 Aug 2025 (v1), last revised 20 Feb 2026 (this version, v2)] Title:M3OOD: Automatic Selection of Multimodal OOD Detectors Authors:Yuehan Qin, Li Li, Defu Cao, Tiankai Yang, Jiate Li, Yue Zhao View a PDF of the paper titled M3OOD: Automatic Selection of Multimodal OOD Detectors, by Yuehan Qin and 5 other authors View PDF Abstract:Out-of-distribution (OOD) robustness is a critical challenge for modern machine learning systems, particularly as they increasingly operate in multimodal settings involving inputs like video, audio, and sensor data. Currently, many OOD detection methods have been proposed, each with different designs targeting various distribution shifts. A single OOD detector may not prevail across all the scenarios; therefore, how can we automatically select an ideal OOD detection model for different distribution shifts? Due to the inherent unsupervised nature of the OOD detection task, it is difficult to predict model performance and find a universally Best model. Also, systematically comparing models on the new unseen data is costly or even impractical. To address this challenge, we introduce M3OOD, a meta-learning-based framework for OOD detector selection in multimodal settings. Meta learning offers a solution by learning from historical model behaviors, enabling rapid adaptation to new data distribution shifts with minimal supervision. Our approach combines multimodal embeddings with h...
