Taming Cascaded Mixture-of-Experts for Modality-missing Multi-modal Salient Object Detection - Wang, Kunpeng; Sun, Feifan; Chen, Keke (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Multi-modal Salient Object Detection (SOD) shows an improvement over its uni-modal counterpart by exploiting the complementary benefits between modalities. However, this improvement relies on complete multi-modal information, which is difficult to be guaranteed in practice due to sensor failures and transmission errors. To address this issue, we propose a robust multi-modal SOD framework that enhances the adaptability to modality-missing conditions, while maintaining comparable performance in the modality-complete condition. Nevertheless, flexibly handling modality-missing and modality-complete cases and integrating their corresponding multi-modal features in a unified framework is non-trivial. To this end, we achieve this framework by designing a Cascaded Mixture-of-Experts (CMoE) network that sequentially incorporates missing-aware and multi-modal MoE. Specifically, the missing-aware MoE employs three modality-reconstruction experts with a soft router to adaptively reconstruct feature representations for both missing and available modalities, assisted by an expert modulation loss that guides the router to assign expert weights according to missing conditions. The multi-modal MoE adopts two homogeneous uni-modal experts with learned modality-specific knowledge tailored for integrating modality features, which are dynamically combined via the soft router. The cascaded architecture fully empowers CMoE with the flexibility across varying input cases. Extensive experiments on modality-missing and modality-complete conditions demonstrate the effectiveness of the proposed method.