用专家混合模型生成未见类点云特征，提升机器人协作环境语义分割能力
GZSL-MoE: Apprentissage G{é}n{é}ralis{é} Z{é}ro-Shot bas{é} sur le M{é}lange d'Experts pour la Segmentation S{é}mantique de Nuages de Points 3DAppliqu{é} {à} un Jeu de Donn{é}es d'Environnement de Collaboration Humain-Robot
- 引入专家混合架构增强生成模型，模拟未见类别真实特征
- 在COVERED数据集上实现可见与不可见类别的双提升性能
- 适合缺乏完整标注数据的机器人交互场景应用
生成式零样本学习（GZSL）在3D点云语义分割任务中展现出巨大潜力。GZSL利用GAN或VAE等生成模型合成未见类别的真实特征，使模型在仅训练见过类别的情况下仍可识别未见类别。本文提出基于专家混合（Mixture-of-Experts, MoE）的通用零样本学习模型（GZSL-MoE），将MoE层嵌入生成器与判别器中，生成与预训练KPConv模型在已见类别上提取的真实特征高度相似的虚假特征。该方法应用于人机协作环境数据集COVERED（CollabOratiVE Robot Environment Dataset），通过结合生成式零样本学习与专家混合机制，显著提升了3D点云语义分割在可见与未见类别上的表现，为缺乏全面训练数据的复杂3D环境理解提供了可行方案。
原文摘要 · Abstract (English)
Generative Zero-Shot Learning approach (GZSL) has demonstrated significant potential in 3D point cloud semantic segmentation tasks. GZSL leverages generative models like GANs or VAEs to synthesize realistic features (real features) of unseen classes. This allows the model to label unseen classes during testing, despite being trained only on seen classes. In this context, we introduce the Generalized Zero-Shot Learning based-upon Mixture-of-Experts (GZSL-MoE) model. This model incorporates Mixture-of-Experts layers (MoE) to generate fake features that closely resemble real features extracted using a pre-trained KPConv (Kernel Point Convolution) model on seen classes. The main contribution of this paper is the integration of Mixture-of-Experts into the Generator and Discriminator components of the Generative Zero-Shot Learning model for 3D point cloud semantic segmentation, applied to the COVERED dataset (CollabOratiVE Robot Environment Dataset) for Human-Robot Collaboration (HRC) environments. By combining the Generative Zero-Shot Learning model with Mixture-of- Experts, GZSL-MoE for 3D point cloud semantic segmentation provides a promising solution for understanding complex 3D environments, especially when comprehensive training data for all object classes is unavailable. The performance evaluation of the GZSL-MoE model highlights its ability to enhance performance on both seen and unseen classes. Keywords Generalized Zero-Shot Learning (GZSL), 3D Point Cloud, 3D Semantic Segmentation, Human-Robot Collaboration, COVERED (CollabOratiVE Robot Environment Dataset), KPConv, Mixture-of Experts
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