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Found 8 papers, 4 papers with code
CUS3D :CLIP-based Unsupervised 3D Segmentation via Object-level Denoise
To ease the difficulty of acquiring annotation labels in 3D data, a common method is using unsupervised and open-vocabulary semantic segmentation, which leverage 2D CLIP semantic knowledge.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
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Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification
Many domain adaptation methods are designed to reduce the distributional differences between sites to some extent, but usually ignore overfitting problem of the model on the source domain.
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention
MQENet treats the mesh evaluation task as a graph classification task for classifying the quality of the input structured mesh.
Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification
In the personalized branch, we integrate vectorized demographic information (i. e., age, gender, and education years) and functional connectivity networks to preserve site-specific characteristics.
ComQA:Compositional Question Answering via Hierarchical Graph Neural Networks
In compositional question answering, the systems should assemble several supporting evidence from the document to generate the final answer, which is more difficult than sentence-level or phrase-level QA.
ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion
The release of ReCO consists of 300k questions that to our knowledge is the largest in Chinese reading comprehension.
