Search Results for author: Zhongchao shi

Found 18 papers, 5 papers with code

Learning to Learn Domain-invariant Parameters for Domain Generalization

no code implementations4 Nov 2022 Feng Hou, Yao Zhang, Yang Liu, Jin Yuan, Cheng Zhong, Yang Zhang, Zhongchao shi, Jianping Fan, Zhiqiang He

Due to domain shift, deep neural networks (DNNs) usually fail to generalize well on unknown test data in practice.

Domain Generalization

SAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency

1 code implementation CVPR 2023 Yang Liu, Yao Zhang, Yixin Wang, Yang Zhang, Jiang Tian, Zhongchao shi, Jianping Fan, Zhiqiang He

To bridge the gap between the reference points of salient queries and Transformer detectors, we propose SAlient Point-based DETR (SAP-DETR) by treating object detection as a transformation from salient points to instance objects.

object-detection Object Detection

Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images

no code implementations26 May 2022 Yao Zhang, Jiawei Yang, Yang Liu, Jiang Tian, Siyun Wang, Cheng Zhong, Zhongchao shi, Yang Zhang, Zhiqiang He

In this paper, we propose a Decoupled Pyramid Correlation Network (DPC-Net) that exploits attention mechanisms to fully leverage both low- and high-level features embedded in FCN to segment liver tumor.

Computed Tomography (CT) Image Segmentation +2

Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation

no code implementations8 Apr 2022 Jin Yuan, Feng Hou, Yangzhou Du, Zhongchao shi, Xin Geng, Jianping Fan, Yong Rui

Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data, and multi-source domain adaptation (MSDA) is very attractive for real world applications.

Domain Adaptation Self-Supervised Learning

Graph Attention Transformer Network for Multi-Label Image Classification

no code implementations8 Mar 2022 Jin Yuan, Shikai Chen, Yao Zhang, Zhongchao shi, Xin Geng, Jianping Fan, Yong Rui

Subsequently, we design the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain.

Classification Graph Attention +2

A Survey of Visual Transformers

1 code implementation11 Nov 2021 Yang Liu, Yao Zhang, Yixin Wang, Feng Hou, Jin Yuan, Jiang Tian, Yang Zhang, Zhongchao shi, Jianping Fan, Zhiqiang He

Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP).

Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation

2 code implementations21 Jul 2021 Yao Zhang, Jiawei Yang, Jiang Tian, Zhongchao shi, Cheng Zhong, Yang Zhang, Zhiqiang He

To this end, we propose a novel mutual learning (ML) strategy for effective and robust multi-modal liver tumor segmentation.

Computed Tomography (CT) Image Segmentation +2

ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities

2 code implementations28 Jun 2021 Yixin Wang, Yang Zhang, Yang Liu, Zihao Lin, Jiang Tian, Cheng Zhong, Zhongchao shi, Jianping Fan, Zhiqiang He

Specifically, ACN adopts a novel co-training network, which enables a coupled learning process for both full modality and missing modality to supplement each other's domain and feature representations, and more importantly, to recover the `missing' information of absent modalities.

Brain Tumor Segmentation Transfer Learning +1

Trust It or Not: Confidence-Guided Automatic Radiology Report Generation

no code implementations21 Jun 2021 Yixin Wang, Zihao Lin, Zhe Xu, Haoyu Dong, Jiang Tian, Jie Luo, Zhongchao shi, Yang Zhang, Jianping Fan, Zhiqiang He

Experimental results have demonstrated that the proposed method for model uncertainty characterization and estimation can produce more reliable confidence scores for radiology report generation, and the modified loss function, which takes into account the uncertainties, leads to better model performance on two public radiology report datasets.

Decision Making Image Captioning +1

Double-Uncertainty Weighted Method for Semi-supervised Learning

no code implementations19 Oct 2020 Yixin Wang, Yao Zhang, Jiang Tian, Cheng Zhong, Zhongchao shi, Yang Zhang, Zhiqiang He

We train the teacher model using Bayesian deep learning to obtain double-uncertainty, i. e. segmentation uncertainty and feature uncertainty.

Disentangled Neural Architecture Search

no code implementations24 Sep 2020 Xinyue Zheng, Peng Wang, Qigang Wang, Zhongchao shi

However, existing methods rely heavily on a black-box controller to search architectures, which suffers from the serious problem of lacking interpretability.

Neural Architecture Search

Challenge Closed-book Science Exam: A Meta-learning Based Question Answering System

no code implementations26 Apr 2020 Xinyue Zheng, Peng Wang, Qigang Wang, Zhongchao shi

Prior work in standardized science exams requires support from large text corpus, such as targeted science corpus fromWikipedia or SimpleWikipedia.

Language Modelling Large Language Model +3

Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database

no code implementations1 Nov 2019 Yao Zhang, Cheng Zhong, Yang Zhang, Zhongchao shi, Zhiqiang He

In the SFAN, a Semantic Attention Transmission (SAT) module is designed to select discriminative low-level localization details with the guidance of neighboring high-level semantic information.

Computed Tomography (CT) Tumor Segmentation

Efficient Automatic Meta Optimization Search for Few-Shot Learning

no code implementations6 Sep 2019 Xinyue Zheng, Peng Wang, Qigang Wang, Zhongchao shi, Feiyu Xu

NAS automatically generates and evaluates meta-learner's architecture for few-shot learning problems, while the meta-learner uses meta-learning algorithm to optimize its parameters based on the distribution of learning tasks.

Few-Shot Learning Neural Architecture Search

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