Search Results for author: Zhongying Deng

Found 17 papers, 9 papers with code

HAMLET: Graph Transformer Neural Operator for Partial Differential Equations

no code implementations5 Feb 2024 Andrey Bryutkin, Jiahao Huang, Zhongying Deng, Guang Yang, Carola-Bibiane Schönlieb, Angelica Aviles-Rivero

We present a novel graph transformer framework, HAMLET, designed to address the challenges in solving partial differential equations (PDEs) using neural networks.

TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios

no code implementations30 Nov 2023 Lihao Liu, Yanqi Cheng, Zhongying Deng, Shujun Wang, Dongdong Chen, Xiaowei Hu, Pietro Liò, Carola-Bibiane Schönlieb, Angelica Aviles-Rivero

Multi-object tracking in traffic videos is a crucial research area, offering immense potential for enhancing traffic monitoring accuracy and promoting road safety measures through the utilisation of advanced machine learning algorithms.

Multi-Object Tracking Object

SAM-Med3D

1 code implementation23 Oct 2023 Haoyu Wang, Sizheng Guo, Jin Ye, Zhongying Deng, Junlong Cheng, Tianbin Li, Jianpin Chen, Yanzhou Su, Ziyan Huang, Yiqing Shen, Bin Fu, Shaoting Zhang, Junjun He, Yu Qiao

These issues can hardly be addressed by fine-tuning SAM on medical data because the original 2D structure of SAM neglects 3D spatial information.

3D Architecture Image Segmentation +1

A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation

2 code implementations7 Sep 2023 Ziyan Huang, Zhongying Deng, Jin Ye, Haoyu Wang, Yanzhou Su, Tianbin Li, Hui Sun, Junlong Cheng, Jianpin Chen, Junjun He, Yun Gu, Shaoting Zhang, Lixu Gu, Yu Qiao

To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation.

Organ Segmentation Segmentation

Generative Model Based Noise Robust Training for Unsupervised Domain Adaptation

no code implementations10 Mar 2023 Zhongying Deng, Da Li, Junjun He, Yi-Zhe Song, Tao Xiang

D-CFA minimizes the domain gap by augmenting the source data with distribution-sampled target features, and trains a noise-robust discriminative classifier by using target domain knowledge from the generative models.

Unsupervised Domain Adaptation

FCN+: Global Receptive Convolution Makes FCN Great Again

no code implementations8 Mar 2023 Zhongying Deng, Xiaoyu Ren, Jin Ye, Junjun He, Yu Qiao

The motivation of GRC is that different channels of a convolutional filter can have different grid sampling locations across the whole input feature map.

Segmentation Semantic Segmentation

NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning

1 code implementation17 Nov 2022 Zhongying Deng, Rihuan Ke, Carola-Bibiane Schonlieb, Angelica I Aviles-Rivero

Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set and massive amounts of unlabeled data.

TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation

no code implementations17 Nov 2022 Zhongying Deng, Yanqi Chen, Lihao Liu, Shujun Wang, Rihuan Ke, Carola-Bibiane Schonlieb, Angelica I Aviles-Rivero

Firstly, TrafficCAM provides both pixel-level and instance-level semantic labelling along with a large range of types of vehicles and pedestrians.

Instance Segmentation Management +1

Robust Target Training for Multi-Source Domain Adaptation

1 code implementation4 Oct 2022 Zhongying Deng, Da Li, Yi-Zhe Song, Tao Xiang

Given any existing fully-trained one-step MSDA model, BORT$^2$ turns it to a labeling function to generate pseudo-labels for the target data and trains a target model using pseudo-labeled target data only.

Domain Adaptation

An evaluation of U-Net in Renal Structure Segmentation

no code implementations6 Sep 2022 Haoyu Wang, Ziyan Huang, Jin Ye, Can Tu, Yuncheng Yang, Shiyi Du, Zhongying Deng, Chenglong Ma, Jingqi Niu, Junjun He

Renal structure segmentation from computed tomography angiography~(CTA) is essential for many computer-assisted renal cancer treatment applications.

Image Segmentation Medical Image Segmentation +2

Dynamic Instance Domain Adaptation

1 code implementation9 Mar 2022 Zhongying Deng, Kaiyang Zhou, Da Li, Junjun He, Yi-Zhe Song, Tao Xiang

In this paper, we address both single-source and multi-source UDA from a completely different perspective, which is to view each instance as a fine domain.

Unsupervised Domain Adaptation

Domain Attention Consistency for Multi-Source Domain Adaptation

1 code implementation6 Nov 2021 Zhongying Deng, Kaiyang Zhou, Yongxin Yang, Tao Xiang

Importantly, the attention module is supervised by a consistency loss, which is imposed on the distributions of channel attention weights between source and target domains.

Attribute Domain Adaptation

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