Problem
Research questions and friction points this paper is trying to address.
few-shot 3D recognition
multi-modal priors
channel redundancy
task-irrelevant noise
modality reliability
Refine Then Fusion: Training-Free 3D Point Cloud Adaptation with Priority Refinement and Multi-Modal Knowledge Fusion
🤖 AI Summary
为解决预训练模型在少样本3D识别任务中的特征冗余和模态可靠性问题，提出了一种无需训练的框架RTF，通过优先精炼和多模态知识融合来提升性能。
📝 Abstract
Recent pre-trained foundation models provide rich multi-modal priors for downstream 3D vision tasks. However, the effectiveness of these representations in few-shot scenarios is limited by two fundamental challenges: High-dimensional features often contain substantial channel redundancy and task-irrelevant noise, while the reliability of different modalities varies across samples. Consequently, direct aggregation of heterogeneous representations overlooks sample-dependent modality reliability and may obscure the discriminative cues essential. To address these limitations, we propose Refine Then Fusion(RTF), a training-free framework for few-shot 3D recognition. RTF first identifies discriminative feature channels by jointly modeling inter-class similarity and intra-class stability, thereby decoupling domain-specific knowledge refinement from the cached representations of pre-trained models. It then introduces a reliability-aware fusion mechanism that estimates sample-wise modality reliability from the distribution shifts induced by feature refinement, enabling adaptive aggregation of multi-modal representations. Furthermore, RTF constructs a memory cache that integrates instance-level support features with class-level prototypes to infer query labels. Extensive experiments on five benchmarks demonstrate that RTF consistently outperforms single-modal baselines, partial-fusion variants, and existing lightweight adaptation methods, achieving state-of-the-art few-shot 3D recognition performance without gradient optimization, additional training data, auxiliary training, or parameter updates.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Training-Free Framework
Priority Refinement
Reliability-Aware Fusion
Multi-Modal Knowledge Fusion
Few-Shot 3D Recognition
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