no code implementations • 10 Apr 2024 • Zhenxi Zhang, Heng Zhou, Xiaoran Shi, Ran Ran, Chunna Tian, Feng Zhou
Additionally, the evidential fusion branch capitalizes on the complementary attributes of the first two branches and leverages an evidence-based Dempster-Shafer fusion strategy, supervised by more reliable and accurate pseudo-labels of unlabeled data.
no code implementations • 18 Oct 2023 • Yanyu Ye, Zhenxi Zhang, Wei Wei, Chunna Tian
To improve the performance of test-time domain adaptation, we propose a multi task consistency guided source-free test-time domain adaptation medical image segmentation method which ensures the consistency of the local boundary predictions and the global prototype representation.
no code implementations • 25 May 2023 • Zhenxi Zhang, Ran Ran, Chunna Tian, Heng Zhou, Fan Yang, Xin Li, Zhicheng Jiao
This paper proposes a cross-supervised learning framework based on dual classifiers (DC-Net), including an evidential classifier and a vanilla classifier.
no code implementations • 25 May 2023 • Zhenxi Zhang, Ran Ran, Chunna Tian, Heng Zhou, Xin Li, Fan Yang, Zhicheng Jiao
To address these issues, we propose a self-aware and cross-sample prototypical learning method (SCP-Net) to enhance the diversity of prediction in consistency learning by utilizing a broader range of semantic information derived from multiple inputs.
no code implementations • NeurIPS 2023 • Yuxuan Ding, Chunna Tian, Haoxuan Ding, Lingqiao Liu
The Stable Diffusion model is a prominent text-to-image generation model that relies on a text prompt as its input, which is encoded using the Contrastive Language-Image Pre-Training (CLIP).
no code implementations • 21 Sep 2022 • Heng Zhou, Chunna Tian, Zhenxi Zhang, Chengyang Li, Yuxuan Ding, Yongqiang Xie, Zhongbo Li
FRDF utilizes the directional information between object pixels to effectively enhance the intra-class compactness of salient regions.
no code implementations • 19 Jul 2022 • Yuxuan Ding, Lingqiao Liu, Chunna Tian, Jingyuan Yang, Haoxuan Ding
The Contrastive Language-Image Pre-training (CLIP) Model is a recently proposed large-scale pre-train model which attracts increasing attention in the computer vision community.
no code implementations • 3 Jun 2022 • Zhenxi Zhang, Chunna Tian, Zhicheng Jiao
In specific, mutual-prototype alignment enhances the information interaction between labeled and unlabeled data.
no code implementations • 26 May 2022 • Heng Zhou, Chunna Tian, Zhenxi Zhang, Chengyang Li, Yongqiang Xie, Zhongbo Li
FNs-player and FPs-player are designed with different strategies: One is to minimize FNs and the other is to minimize FPs.
no code implementations • 25 Jun 2020 • Zhenxi Zhang, Chunna Tian, Jie Li, Zhusi Zhong, Zhicheng Jiao, Xinbo Gao
Further, we propose a context encoding module to utilize the global predictor from the error map to enhance the feature representation and regularize the networks.
no code implementations • CVPR 2015 • Lei Zhang, Wei Wei, Yanning Zhang, Chunna Tian, Fei Li
To address this problem, a novel reweighted Laplace prior based hyperspectral compressive sensing method is proposed in this study.