Towards Robust Multimodal Learning in the Open World

TL;DR

This paper investigates the challenges of making multimodal neural networks robust and reliable in unpredictable open-world environments, addressing issues like incomplete data and distribution shifts.

Contribution

It identifies key robustness challenges in open-world multimodal learning and proposes strategies to improve system reliability in real-world scenarios.

Findings

Highlighting the limitations of current models in open environments

Proposing methods to enhance robustness against distribution shifts

Demonstrating improved performance in real-world tests

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.