Search Results for author: Jin Ye

Found 20 papers, 10 papers with code


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

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

no code implementations14 Sep 2023 Fei Dou, Jin Ye, Geng Yuan, Qin Lu, Wei Niu, Haijian Sun, Le Guan, Guoyu Lu, Gengchen Mai, Ninghao Liu, Jin Lu, Zhengliang Liu, Zihao Wu, Chenjiao Tan, Shaochen Xu, Xianqiao Wang, Guoming Li, Lilong Chai, Sheng Li, Jin Sun, Hongyue Sun, Yunli Shao, Changying Li, Tianming Liu, WenZhan Song

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas.

Decision Making

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

Artifact Restoration in Histology Images with Diffusion Probabilistic Models

1 code implementation26 Jul 2023 Zhenqi He, Junjun He, Jin Ye, Yiqing Shen

Histological whole slide images (WSIs) can be usually compromised by artifacts, such as tissue folding and bubbles, which will increase the examination difficulty for both pathologists and Computer-Aided Diagnosis (CAD) systems.

Denoising whole slide images

Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation

1 code implementation22 Jul 2023 Yuncheng Yang, Meng Wei, Junjun He, Jie Yang, Jin Ye, Yun Gu

To make up for its deficiency when applying transfer learning to medical image segmentation, in this paper, we therefore propose a new Transferability Estimation (TE) method.

Image Segmentation Medical Image Segmentation +3

CityTrack: Improving City-Scale Multi-Camera Multi-Target Tracking by Location-Aware Tracking and Box-Grained Matching

no code implementations6 Jul 2023 Jincheng Lu, Xipeng Yang, Jin Ye, Yifu Zhang, Zhikang Zou, Wei zhang, Xiao Tan

Targets in urban traffic scenes often undergo occlusion, illumination changes, and perspective changes, making it difficult to associate targets across different cameras accurately.

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

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

Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network

no code implementations27 Jul 2021 Zhikang Zou, Xiaoye Qu, Pan Zhou, Shuangjie Xu, Xiaoqing Ye, Wenhao Wu, Jin Ye

In specific, at the coarse-grained stage, we design a dual-discriminator strategy to adapt source domain to be close to the targets from the perspectives of both global and local feature space via adversarial learning.

Crowd Counting Transfer Learning

Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition

1 code implementation ECCV 2020 Jin Ye, Junjun He, Xiaojiang Peng, Wenhao Wu, Yu Qiao

To this end, we propose an Attention-Driven Dynamic Graph Convolutional Network (ADD-GCN) to dynamically generate a specific graph for each image.

Multi-Label Classification

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