{"type":"ScholarlyArticle","title":"Rethinking Multimodal Few-Shot 3D Point Cloud Segmentation: From Fused Refinement to Decoupled Arbitration","authors":[{"name":"Wentao Bian","slug":"wentao-bian","given":"Wentao","family":"Bian","affiliation":""},{"name":"Fenglei Xu","slug":"fenglei-xu","given":"Fenglei","family":"Xu","affiliation":""}],"abstract":"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, Contrastive Language-Image Pre-training (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 the baseline MultiModal Few-Shot SegNet (MM-FSS) to generate text embeddings, which can increase cost-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/.","doi":"10.24963/ijcai.2026/103","url":"https://ijcai.org/proceedings/2026/103","pdf":"https://ijcai.org/proceedings/2026/0103.pdf","markdown":"https://ijcai.org/proceedings/2026/103.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/103","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":103,"pages":{"first":917,"last":925},"keywords":["Computer Vision: 3D computer vision","Computer Vision: Vision, language and reasoning","Computer Vision: Segmentation, grouping and shape analysis","Computer Vision: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: Multimodal learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}