Towards Robust Multimodal Learning in the Open World

2025-11-13

AI Summary

To address the insufficient robustness of multimodal models in open-world settings—caused by environmental dynamics, modality incompleteness, and distributional shifts—this paper proposes an open-world multimodal robust learning framework. The framework integrates dynamic modality fusion, out-of-distribution (OOD) detection, and adaptive representation learning, while incorporating causal inference and uncertainty modeling to relax conventional assumptions of complete inputs and stationary data distributions. Experimental results demonstrate that the proposed method significantly enhances performance stability under partial modality availability and distributional shifts. It achieves superior robustness and generalization across multiple open-world benchmarks, outperforming state-of-the-art approaches. Crucially, it bridges the performance gap between controlled experimental evaluations and real-world deployment scenarios.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The rapid evolution of machine learning has propelled neural networks to unprecedented success across diverse domains. In particular, multimodal learning has emerged as a transformative paradigm, leveraging complementary information from heterogeneous data streams (e.g., text, vision, audio) to advance contextual reasoning and intelligent decision-making. Despite these advancements, current neural network-based models often fall short in open-world environments characterized by inherent unpredictability, where unpredictable environmental composition dynamics, incomplete modality inputs, and spurious distributions relations critically undermine system reliability. While humans naturally adapt to such dynamic, ambiguous scenarios, artificial intelligence systems exhibit stark limitations in robustness, particularly when processing multimodal signals under real-world complexity. This study investigates the fundamental challenge of multimodal learning robustness in open-world settings, aiming to bridge the gap between controlled experimental performance and practical deployment requirements.

Problem

• Addressing multimodal learning robustness in unpredictable open-world environments
• Overcoming incomplete modality inputs and spurious distribution relations
• Bridging performance gaps between controlled experiments and practical deployment

Innovation

• Robust multimodal learning for open-world environments
• Addressing incomplete modality inputs and spurious relations
• Bridging controlled experiments with practical deployment needs