MAGNeT: Multimodal Adaptive Gaussian Networks for Intent Inference in Moving Target Selection across Complex Scenarios

TL;DR

MAGNeT introduces a novel multimodal adaptive Gaussian network that effectively infers moving targets across diverse scenarios with minimal training, leveraging real-time contextual fusion for improved accuracy.

Contribution

It combines classical Gaussian models with context-aware multimodal fusion, enabling transferability and adaptability in dynamic multimedia environments.

Findings

MAGNeT outperforms existing models with fewer samples.

It achieves lower error rates in complex scenarios.

The method maintains interpretability through Gaussian modeling.

Abstract

Moving target selection in multimedia interactive systems faces unprecedented challenges as users increasingly interact across diverse and dynamic contexts-from live streaming in moving vehicles to VR gaming in varying environments. Existing approaches rely on probabilistic models that relate endpoint distribution to target properties such as size and speed. However, these methods require substantial training data for each new context and lack transferability across scenarios, limiting their practical deployment in diverse multimedia environments where rich multimodal contextual information is readily available. This paper introduces MAGNeT (Multimodal Adaptive Gaussian Networks), which addresses these problems by combining classical statistical modeling with a context-aware multimodal method. MAGNeT dynamically fuses pre-fitted Ternary-Gaussian models from various scenarios based on real-time contextual cues, enabling effective adaptation with minimal training data while preserving model interpretability. We conduct experiments on self-constructed 2D and 3D moving target selection datasets under in-vehicle vibration conditions. Extensive experiments demonstrate that MAGNeT achieves lower error rates with few-shot samples by applying context-aware fusion of Gaussian experts from multi-factor conditions.