Search Results for author: Meng Ye

Found 10 papers, 4 papers with code

DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method

no code implementations14 Jun 2022 Qi Chang, Zhennan Yan, Mu Zhou, Di Liu, Khalid Sawalha, Meng Ye, Qilong Zhangli, Mikael Kanski, Subhi Al Aref, Leon Axel, Dimitris Metaxas

Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns.

3D Reconstruction 3D Shape Reconstruction +4

Modular Adaptation for Cross-Domain Few-Shot Learning

1 code implementation1 Apr 2021 Xiao Lin, Meng Ye, Yunye Gong, Giedrius Buracas, Nikoletta Basiou, Ajay Divakaran, Yi Yao

Adapting pre-trained representations has become the go-to recipe for learning new downstream tasks with limited examples.

cross-domain few-shot learning Representation Learning

PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data

no code implementations18 Aug 2020 Meng Ye, Qiaoying Huang, Dong Yang, Pengxiang Wu, Jingru Yi, Leon Axel, Dimitris Metaxas

The 3D volumetric shape of the heart's left ventricle (LV) myocardium (MYO) wall provides important information for diagnosis of cardiac disease and invasive procedure navigation.

Image Segmentation Semantic Segmentation

Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection

no code implementations7 Aug 2018 Meng Ye, Yuhong Guo

The approach projects the label embedding vectors into a low-dimensional space to induce better inter-label relationships and explicitly facilitate information transfer from seen labels to unseen labels, while simultaneously learning a max-margin multi-label classifier with the projected label embeddings.

Multi-Label Image Classification Multi-label zero-shot learning +1

Progressive Ensemble Networks for Zero-Shot Recognition

no code implementations CVPR 2019 Meng Ye, Yuhong Guo

The ensemble network is built by learning multiple image classification functions with a shared feature extraction network but different label embedding representations, which enhance the diversity of the classifiers and facilitate information transfer to unlabeled classes.

Generalized Zero-Shot Learning Image Classification

Deep Triplet Ranking Networks for One-Shot Recognition

1 code implementation19 Apr 2018 Meng Ye, Yuhong Guo

Despite the breakthroughs achieved by deep learning models in conventional supervised learning scenarios, their dependence on sufficient labeled training data in each class prevents effective applications of these deep models in situations where labeled training instances for a subset of novel classes are very sparse -- in the extreme case only one instance is available for each class.

One-Shot Learning

Zero-Shot Classification With Discriminative Semantic Representation Learning

no code implementations CVPR 2017 Meng Ye, Yuhong Guo

The proposed approach aims to identify a set of common high-level semantic components across the two domains via non-negative sparse matrix factorization, while enforcing the representation vectors of the images in this common component-based space to be discriminatively aligned with the attribute-based label representation vectors.

Classification General Classification +3

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