Search Results for author: Jianzhong He

Found 16 papers, 3 papers with code

Bundle-specific Tractogram Distribution Estimation Using Higher-order Streamline Differential Equation

no code implementations6 Jul 2023 Yuanjing Feng, Lei Xie, Jingqiang Wang, Jianzhong He, Fei Gao

At the global level, the tractography process is simplified as the estimation of bundle-specific tractogram distribution (BTD) coefficients by minimizing the energy optimization model, and is used to characterize the relations between BTD and diffusion tensor vector under the prior guidance by introducing the tractogram bundle information to provide anatomic priors.

Bayesian Evidential Learning for Few-Shot Classification

no code implementations19 Jul 2022 Xiongkun Linghu, Yan Bai, Yihang Lou, Shengsen Wu, Jinze Li, Jianzhong He, Tao Bai

Few-Shot Classification(FSC) aims to generalize from base classes to novel classes given very limited labeled samples, which is an important step on the path toward human-like machine learning.

Classification Metric Learning +1

White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning

no code implementations6 Jul 2022 Yuqian Chen, Fan Zhang, Chaoyi Zhang, Tengfei Xue, Leo R. Zekelman, Jianzhong He, Yang song, Nikos Makris, Yogesh Rathi, Alexandra J. Golby, Weidong Cai, Lauren J. O'Donnell

In this paper, we propose a deep-learning-based framework for neuropsychological score prediction using microstructure measurements estimated from diffusion magnetic resonance imaging (dMRI) tractography, focusing on predicting performance on a receptive vocabulary assessment task based on a critical fiber tract for language, the arcuate fasciculus (AF).

Memory-Based Label-Text Tuning for Few-Shot Class-Incremental Learning

no code implementations3 Jul 2022 Jinze Li, Yan Bai, Yihang Lou, Xiongkun Linghu, Jianzhong He, Shaoyun Xu, Tao Bai

The difficulties are that training on a sequence of limited data from new tasks leads to severe overfitting issues and causes the well-known catastrophic forgetting problem.

Few-Shot Class-Incremental Learning Incremental Learning

Switchable Representation Learning Framework with Self-compatibility

no code implementations CVPR 2023 Shengsen Wu, Yan Bai, Yihang Lou, Xiongkun Linghu, Jianzhong He, Ling-Yu Duan

Existing research mainly focuses on the one-to-one compatible paradigm, which is limited in learning compatibility among multiple models.

Representation Learning

Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation

no code implementations CVPR 2022 Liang Chen, Yihang Lou, Jianzhong He, Tao Bai, Minghua Deng

Therefore, in this paper, we propose a Geometric anchor-guided Adversarial and conTrastive learning framework with uncErtainty modeling called GATE to alleviate these issues.

Contrastive Learning Universal Domain Adaptation

T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation

1 code implementation ICCV 2021 Ruihuang Li, Xu Jia, Jianzhong He, Shuaijun Chen, QinGhua Hu

Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help improve performance on target domain.

Unsupervised Domain Adaptation

ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Image Segmentation

no code implementations CVPR 2021 Xinyue Huo, Lingxi Xie, Jianzhong He, Zijie Yang, Wengang Zhou, Houqiang Li, Qi Tian

Semi-supervised learning is a useful tool for image segmentation, mainly due to its ability in extracting knowledge from unlabeled data to assist learning from labeled data.

Continual Learning Image Segmentation +3

Multi-Target Domain Adaptation with Collaborative Consistency Learning

no code implementations CVPR 2021 Takashi Isobe, Xu Jia, Shuaijun Chen, Jianzhong He, Yongjie Shi, Jianzhuang Liu, Huchuan Lu, Shengjin Wang

To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights.

Multi-target Domain Adaptation Semantic Segmentation +1

ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation

no code implementations24 Jun 2020 Xinyue Huo, Lingxi Xie, Jianzhong He, Zijie Yang, Qi Tian

This paper focuses on a popular pipeline known as self learning, and points out a weakness named lazy learning that refers to the difficulty for a model to learn from the pseudo labels generated by itself.

Autonomous Driving Image Segmentation +4

Bi-Directional Cascade Network for Perceptual Edge Detection

2 code implementations CVPR 2019 Jianzhong He, Shiliang Zhang, Ming Yang, Yanhu Shan, Tiejun Huang

Exploiting multi-scale representations is critical to improve edge detection for objects at different scales.

Edge Detection

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