no code implementations • 13 Aug 2023 • Jinghua Zhang, Li Liu, Olli Silvén, Matti Pietikäinen, Dewen Hu
In our in-depth examination, we delve into various facets of FSCIL, encompassing the problem definition, the discussion of the primary challenges of unreliable empirical risk minimization and the stability-plasticity dilemma, general schemes, and relevant problems of IL and Few-shot Learning (FSL).
class-incremental learning
Class-Incremental Object Detection
+6
1 code implementation • 24 Apr 2023 • Jinghua Zhang, Li Liu, Kai Gao, Dewen Hu
In forward-compatible learning, we propose an innovative virtual class synthesis strategy and a Center-Triplet (CT) loss to enhance discriminative feature learning.
class-incremental learning
Few-Shot Class-Incremental Learning
+6
no code implementations • 15 Mar 2023 • Yimin Yin, Siliang He, Renye Zhang, Hongli Chang, Xu Han, Jinghua Zhang
This paper collects 120 relevant papers to summarize the development of iris recognition based on deep learning.
no code implementations • 15 Jan 2023 • Ao Chen, Jinghua Zhang, Md Mamunur Rahaman, Hongzan Sun, M. D., Tieyong Zeng, Marcin Grzegorzek, Feng-Lei Fan, Chen Li
The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and resolution of the video, and similarity of sperms and impurities.
no code implementations • 29 Aug 2022 • Yimin Yin, Renye Zhang, PengFei Liu, Wanxia Deng, Siliang He, Chen Li, Jinghua Zhang
To our best knowledge, this paper is the first comprehensive survey focusing on finger vein recognition based on artificial neural networks.
no code implementations • 5 Jul 2022 • Renye Zhang, Yimin Yin, Wanxia Deng, Chen Li, Jinghua Zhang
Finger vein image recognition technology plays an important role in biometric recognition and has been successfully applied in many fields.
no code implementations • 4 Apr 2022 • Jiawei Zhang, Xin Zhao, Tao Jiang, Md Mamunur Rahaman, YuDong Yao, Yu-Hao Lin, Jinghua Zhang, Ao Pan, Marcin Grzegorzek, Chen Li
This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy.
no code implementations • 18 Feb 2022 • Jiawei Zhang, Chen Li, Md Mamunur Rahaman, YuDong Yao, Pingli Ma, Jinghua Zhang, Xin Zhao, Tao Jiang, Marcin Grzegorzek
This study has high research significance and application value, which can be referred to microbial researchers to have a comprehensive understanding of microorganism biovolume measurements using digital image analysis methods and potential applications.
no code implementations • 1 Aug 2021 • Jinghua Zhang, Chen Li, Yimin Yin, Jiawei Zhang, Marcin Grzegorzek
Therefore, the automatic image analysis based on artificial neural networks is introduced to optimize it.
no code implementations • 22 Jun 2021 • Hechen Yang, Chen Li, Jinghua Zhang, Peng Zhao, Ao Chen, Xin Zhao, Tao Jiang, Marcin Grzegorzek
We conclude that ViT performs the worst in classifying 8 * 8 pixel patches, but it outperforms most convolutional neural networks in classifying 224 * 224 pixel patches.
no code implementations • 25 Mar 2021 • Jiawei Zhang, Chen Li, Md Mamunur Rahaman, YuDong Yao, Pingli Ma, Jinghua Zhang, Xin Zhao, Tao Jiang, Marcin Grzegorzek
In this article, we have studied the development of microorganism counting methods using digital image analysis.
no code implementations • 9 Mar 2021 • Jinghua Zhang, Chinmoy Kundu, Emi Garcia-Palacios
The backhaul reliability of secondary and the desired outage probability of the primary also have significant impact on the system.
no code implementations • 7 Mar 2021 • Jinghua Zhang, Chinmoy Kundu, Octavia A. Dobre, Emi Garcia-Palacios, Nguyen-Son Vo
{We also propose an optimal selection scheme and compare performances with the sub-optimal selection schemes.}
no code implementations • 24 Feb 2021 • Frank Kulwa, Chen Li, Jinghua Zhang, Kimiaki Shirahama, Sergey Kosov, Xin Zhao, Hongzan Sun, Tao Jiang, Marcin Grzegorzek
In order to fasten, low the cost, increase consistency and accuracy of identification, we propose the novel pairwise deep learning features to analyze microorganisms.
no code implementations • 20 Feb 2021 • Zihan Li, Chen Li, YuDong Yao, Jinghua Zhang, Md Mamunur Rahaman, Hao Xu, Frank Kulwa, Bolin Lu, Xuemin Zhu, Tao Jiang
EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions.
no code implementations • 16 Feb 2021 • Burhan Wafai, Chinmoy Kundu, Ankit Dubey, Jinghua Zhang, Mark F. Flanagan
The small-cell network is operating under a spectrum sharing agreement with a primary network in a cognitive radio system.
no code implementations • 8 Mar 2020 • Jinghua Zhang, Chen Li, Frank Kulwa, Xin Zhao, Changhao Sun, Zihan Li, Tao Jiang, Hong Li
In order to assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multi-scale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper.
no code implementations • 3 Mar 2020 • Changhao Sun, Chen Li, Jinghua Zhang, Muhammad Rahaman, Shiliang Ai, Hao Chen, Frank Kulwa, Yixin Li, Xiaoyan Li, Tao Jiang
This HCRF model is built up with higher order potentials, including pixel-level and patch-level potentials, and graph-based post-processing is applied to further improve its segmentation performance.
Ranked #1 on
Blood Detection
on EGC-FPHFS