Qiankun Li

The work proposes Dual-Adaptive SAM3 (DA-SAM3), which replaces selected feed-forward blocks in SAM3's fusion module with dual-adaptive MoE layers: a task-aware Dynamic Expert Router (DER) sparsely activates experts by jointly reasoning over visual content and the textual concept prompt, while a parameter-aware Decomposed Parameterized Experts (DPE) design represents each expert as a shared frozen base inherited from pretrained SAM3 plus a lightweight trainable low-rank delta, so that on the four public datasets Synapse CT, MMWHS, BTCV and ACDC it matches or exceeds fully fine-tuned SAM3 and standard MoE baselines, reports roughly a 5% gain over then state-of-the-art methods, and reduces MoE parameter overhead by more than 80%.

The work proposes Dual-Adaptive SAM3 (DA-SAM3), which replaces selected feed-forward blocks in SAM3's fusion module with dual-adaptive MoE layers: a task-aware Dynamic Expert Router (DER) sparsely activates experts by jointly reasoning over visual content and the textual concept prompt, while a parameter-aware Decomposed Parameterized Experts (DPE) design represents each expert as a shared frozen base inherited from pretrained SAM3 plus a lightweight trainable low-rank delta, so that on the four public datasets Synapse CT, MMWHS, BTCV and ACDC it matches or exceeds fully fine-tuned SAM3 and standard MoE baselines, reports roughly a 5% gain over then state-of-the-art methods, and reduces MoE parameter overhead by more than 80%.