Search Results for author: Chengfeng Zhou

Found 5 papers, 2 papers with code

Skeleton-Guided Instance Separation for Fine-Grained Segmentation in Microscopy

no code implementations18 Jan 2024 Jun Wang, Chengfeng Zhou, Zhaoyan Ming, Lina Wei, Xudong Jiang, Dahong Qian

One of the fundamental challenges in microscopy (MS) image analysis is instance segmentation (IS), particularly when segmenting cluster regions where multiple objects of varying sizes and shapes may be connected or even overlapped in arbitrary orientations.

Instance Segmentation Semantic Segmentation

Towards Open-set Gesture Recognition via Feature Activation Enhancement and Orthogonal Prototype Learning

no code implementations5 Dec 2023 Chen Liu, Can Han, Chengfeng Zhou, Crystal Cai, Suncheng Xiang, Hualiang Ni, Dahong Qian

While there has been significant progress in gesture recognition based on surface electromyography (sEMG), accurate recognition of predefined gestures only within a closed set is still inadequate in practice.

Gesture Recognition Open Set Learning

Towards Discriminative Representation with Meta-learning for Colonoscopic Polyp Re-Identification

no code implementations2 Aug 2023 Suncheng Xiang, Qingzhong Chen, Shilun Cai, Chengfeng Zhou, Crystal Cai, Sijia Du, Zhengjie Zhang, Yunshi Zhong, Dahong Qian

Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras and plays an important role in the prevention and treatment of colorectal cancer in computer-aided diagnosis.

Meta-Learning Retrieval

Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification

1 code implementation19 Apr 2023 Suncheng Xiang, Jingsheng Gao, Mengyuan Guan, Jiacheng Ruan, Chengfeng Zhou, Ting Liu, Dahong Qian, Yuzhuo Fu

In this paper, we propose a Multi-Modal Equivalent Transformer called MMET for more robust visual-semantic embedding learning on visual, textual and visual-textual tasks respectively.

Generalizable Person Re-identification Representation Learning

A simple normalization technique using window statistics to improve the out-of-distribution generalization on medical images

1 code implementation7 Jul 2022 Chengfeng Zhou, Songchang Chen, Chenming Xu, Jun Wang, Feng Liu, Chun Zhang, Juan Ye, Hefeng Huang, Dahong Qian

In this study, we present a novel normalization technique called window normalization (WIN) to improve the model generalization on heterogeneous medical images, which is a simple yet effective alternative to existing normalization methods.

Breast Cancer Detection Out-of-Distribution Generalization

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