In this paper, we revisit multimodal few-shot 3D point cloud semantic segmentation (FS-PCS), identifying a conflict in "Fuse-then-Refine" paradigms: the "Plasticity-Stability Dilemma." In addition, CLIP's inter-class confusion can result in semantic blindness. To address these issues, we present the Decoupled-experts Arbitration Few-Shot SegNet (DA-FSS), a model that effectively distinguishes between semantic and geometric paths and mutually regularizes their gradients to achieve better generalization. DA-FSS employs the same backbone and pre-trained text encoder as MM-FSS to generate text embeddings, which can increase free modalities' utilization rate and better leverage each modality's information space. To achieve this, we propose a Parallel Expert Refinement module to generate each modal correlation. We also propose a Stacked Arbitration Module (SAM) to perform convolutional fusion and arbitrate correlations for each modality pathway. The Parallel Experts decouple two paths: a Geometric Expert maintains plasticity, and a Semantic Expert ensures stability. They are coordinated via a Decoupled Alignment Module (DAM) that transfers knowledge without propagating confusion. Experiments on popular datasets (S3DIS, ScanNet) demonstrate the superiority of DA-FSS over MM-FSS. Meanwhile, geometric boundaries, completeness, and texture differentiation are all superior to the baseline. The code is available at: https://github.com/MoWenQAQ/DA-FSS.

翻译：本文重新审视了多模态少样本三维点云语义分割（FS-PCS），识别出“先融合后细化”范式中的一个冲突：“可塑性-稳定性困境”。此外，CLIP的类间混淆可能导致语义盲区。为解决这些问题，我们提出了解耦专家仲裁少样本分割网络（DA-FSS），该模型能有效区分语义路径与几何路径，并通过相互正则化其梯度以实现更好的泛化能力。DA-FSS采用与MM-FSS相同的主干网络和预训练文本编码器来生成文本嵌入，这可以提高自由模态的利用率并更好地利用每个模态的信息空间。为此，我们提出了并行专家细化模块来生成各模态相关性。我们还提出了堆叠仲裁模块（SAM）来执行卷积融合并对各模态路径的相关性进行仲裁。并行专家解耦了两条路径：几何专家保持可塑性，语义专家确保稳定性。它们通过一个解耦对齐模块（DAM）进行协调，该模块能在不传播混淆的情况下传递知识。在主流数据集（S3DIS、ScanNet）上的实验证明了DA-FSS相对于MM-FSS的优越性。同时，在几何边界、完整性和纹理区分度方面均优于基线。代码发布于：https://github.com/MoWenQAQ/DA-FSS。