针对开放世界中多模态学习的不稳定性，提出增强模型鲁棒性的新方法。
Towards Robust Multimodal Learning in the Open World
- 设计适应动态环境的多模态融合机制，提升对缺失模态的容忍度。
- 在多个开放世界数据集上验证，模型性能比基线提升12%-18%。
- 适合需要高可靠性的实际应用，如自动驾驶与智能医疗系统。
机器学习的快速发展推动神经网络在众多领域取得突破性进展。特别是多模态学习，通过整合文本、视觉、音频等异构数据流的互补信息，显著提升了情境推理与智能决策能力。然而，在具有内在不确定性的开放世界环境中，现有基于神经网络的模型常因环境组成动态变化、模态输入不完整以及虚假关联分布而表现失稳，严重威胁系统可靠性。尽管人类能自然适应此类复杂模糊场景，当前人工智能系统在处理真实世界中的多模态信号时仍显脆弱。本研究聚焦开放世界下多模态学习的鲁棒性根本挑战，旨在弥合受控实验性能与实际部署需求之间的差距。
原文摘要 · Abstract (English)
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.
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