A multi-scale vision mixture-of-experts for salient object detection with Kolmogorov–Arnold adapter

Diverse domains and object variations make salient object detection (SOD) a challenging task in computer vision. Many previous studies have adopted multi-scale neural networks with attention mechanisms. Although they are popular, the design of their networks lacks sufficient flexibility, which hinders their generalization across objects of different scales and domains. To address the above issue, we propose a novel mixture-of-experts salient object detection (MoESOD) approach. We design a multi-scale mixture-of-experts (MMoE) module, essentially large neural networks, to improve the model’s expressive power and generalization ability without significantly increasing computational cost. By leveraging expert competition and collaboration strategies, the MMoE module effectively integrates contributions from different experts. The MMoE module not only captures multi-scale features but also effectively fuses semantic information across scales through the expert gating mechanism. Additionally, the novel Kolmogorov–Arnold adapter (KAA) is designed to enhance the model’s flexibility, allowing it to adapt easily to SOD tasks across different domains. Comprehensive experiments show that MoESOD consistently achieves higher performance than, or at least comparable performance to, state-of-the-art methods on 17 different SOD benchmarks and 1 downstream tasks. To the best of our knowledge, this is the first study to explore Kolmogorov–Arnold network within the SOD community.