Low resource cross-modal alignment using HGNN to enhance speech representation
本文提出了一种基于异构图神经网络和链接预测的低资源跨模态对齐方法，用于增强语音表示，减少了对大量训练数据的需求。
0 citationsRead paper
Selected work
Representative Papers
Enhancing speech representation learning with cross-modal knowledge transfer with HGNN under low resource settings: the case study of Yemba
研究使用HGNN进行跨模态知识迁移以增强低资源语言的语音表示学习，通过将声学和语言实体建模为不同节点类型，并利用消息传递机制实现知识转移。
0 citationsRead paper
Automated pipeline for herbarium label digitization
为解决标本标签元数据难以大规模访问的问题，本文提出HERBIOME，一种自动化处理流程，利用YOLOv8、CRAFT Hezar、TrOCR和GPT-4o Mini等技术实现标签信息的自动提取与结构化。
0 citationsRead paper
AT-ViT: Area-Targeted Multi-View Vision Transformer with Cross-Attention and Multi-Scale Patching for Plant Trait Recognition in Herbarium Images
为解决标本图像中背景元素干扰植物特征识别的问题，提出AT-ViT模型，采用多尺度、多视角交叉注意力融合和掩码引导的补丁加权机制，提高对植物特征的学习准确性。
0 citationsRead paper
Conditional multivariate functional PCA for the reconstruction of temperature and salinity profiles partially sampled by deep-diving marine mammals
This study addresses the incomplete depth coverage of temperature–salinity profiles collected by deep-diving marine mammals in the Indian Ocean sector of the Southern Ocean, which arises from behavioral differences among individuals. To overcome this limitation, the authors propose a multivariate functional principal component analysis method incorporating geographic covariates. By modeling the mean and covariance structure of complete bivariate profiles, they construct eigenfunction bases and integrate a measurement error model to estimate conditional functional principal scores, enabling high-fidelity reconstruction of truncated profiles across the full depth range. In simulations, the approach improves reconstruction accuracy by 30% for temperature and 33% for salinity in the 20–500 m layer when applied to profiles truncated at 250 m. The method successfully reconstructs approximately 90,000 profiles across a 3-million-square-kilometer region surrounding the French subantarctic islands, marking the first large-scale, accurate recovery of incomplete oceanographic profiles.
0 citationsRead paper
Recent publications
Latest Papers
Low resource cross-modal alignment using HGNN to enhance speech representation
本文提出了一种基于异构图神经网络和链接预测的低资源跨模态对齐方法，用于增强语音表示，减少了对大量训练数据的需求。
0 citationsRead paper
Enhancing speech representation learning with cross-modal knowledge transfer with HGNN under low resource settings: the case study of Yemba
研究使用HGNN进行跨模态知识迁移以增强低资源语言的语音表示学习，通过将声学和语言实体建模为不同节点类型，并利用消息传递机制实现知识转移。
0 citationsRead paper
Automated pipeline for herbarium label digitization
为解决标本标签元数据难以大规模访问的问题，本文提出HERBIOME，一种自动化处理流程，利用YOLOv8、CRAFT Hezar、TrOCR和GPT-4o Mini等技术实现标签信息的自动提取与结构化。
0 citationsRead paper
AT-ViT: Area-Targeted Multi-View Vision Transformer with Cross-Attention and Multi-Scale Patching for Plant Trait Recognition in Herbarium Images
为解决标本图像中背景元素干扰植物特征识别的问题，提出AT-ViT模型，采用多尺度、多视角交叉注意力融合和掩码引导的补丁加权机制，提高对植物特征的学习准确性。
0 citationsRead paper
Conditional multivariate functional PCA for the reconstruction of temperature and salinity profiles partially sampled by deep-diving marine mammals
This study addresses the incomplete depth coverage of temperature–salinity profiles collected by deep-diving marine mammals in the Indian Ocean sector of the Southern Ocean, which arises from behavioral differences among individuals. To overcome this limitation, the authors propose a multivariate functional principal component analysis method incorporating geographic covariates. By modeling the mean and covariance structure of complete bivariate profiles, they construct eigenfunction bases and integrate a measurement error model to estimate conditional functional principal scores, enabling high-fidelity reconstruction of truncated profiles across the full depth range. In simulations, the approach improves reconstruction accuracy by 30% for temperature and 33% for salinity in the 20–500 m layer when applied to profiles truncated at 250 m. The method successfully reconstructs approximately 90,000 profiles across a 3-million-square-kilometer region surrounding the French subantarctic islands, marking the first large-scale, accurate recovery of incomplete oceanographic profiles.
0 citationsRead paper