What is the impact of imbalanced domain generalization techniques (e.g., SMoES routing) on the robustness of m

Abstract

Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have significantly pushed the frontier of egocentric video question answering (EgocentricQA). However, existing benchmarks and studies are mainly limited to common daily activities such as cooking and cleaning. In contrast, real-world deployment inevitably encounters domain shifts, where target domains differ substantially in both visual style and semantic content. To bridge this gap, we introduce EgoCross, a comprehensive benchmark designed to evaluate the cross-domain generalization of MLLMs in EgocentricQA. EgoCross covers four div

Research Question

What is the impact of imbalanced domain generalization techniques (e.g., SMoES routing) on the robustness of multimodal language models when evaluated on cross-domain visual question answering benchmarks with varying modality distributions?

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