Hong Cheng
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Also affiliated: Changchun University of Science and Technology (2012); Hebei Agricultural University (2016); Anhui University (2023); University of Electronic Science and Technology of China (2023); Kwangwoon University (2018–2021); Chinese University of Hong Kong (2024–2025); PLA Air Force Aviation University (2012)
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Hong Cheng's research centers on the application of machine learning and advanced algorithms to complex visual recognition tasks. His work includes developing methods for camouflaged object detection, utilizing techniques such as mutual graph learning and uncertainty-guided transformer reasoning. Cheng has also investigated contour knowledge transfer for salient object detection and explored sparsity-induced similarity measures for label propagation. His publications also touch upon haptic identification using ELM-controlled uncertain manipulators and sparse representation in visual recognition. Cheng has collaborated with researchers at Southern Arkansas University, including Syed M Rahman, Rupesh Konduru, and Wen Cheng, on several shared publications. His work has been recognized with an h-index of 5, with a total of 23 publications and 169 citations.
Metrics
- h-index: 5
- Publications: 23
- Citations: 169
Selected Publications
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CFCT: A Framework Using ConvNeXt With FPN, CBAM and A Topology Enhanced Loss Function for Skin Lesion Segmentation (2026)
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Skin Lesion Segmentation Using Unet With A Topology Term in Loss Function (2025)
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Multi-Class Label Detection and Bounding Box Regression Using Transformer with a Customized Loss Function (2024)
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Object Localization Using Vision Transformer with a Loss Function Based on IOU and Mean Squared Error (2023)
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A Topological Data Analysis-Based Approach to Object Localization: A Comparison with ViT and Yolov7 (2023)
Collaboration Network
Top Collaborators
- Vision Transformers for Medical Images Classifications
- Vision Transformers for Medical Images Classifications
- A Topological Data Analysis-Based Approach to Object Localization: A Comparison with ViT and Yolov7
- Object Localization Using Vision Transformer with a Loss Function Based on IOU and Mean Squared Error
- Multi-Class Label Detection and Bounding Box Regression Using Transformer with a Customized Loss Function
- Skin Lesion Segmentation Using Unet With A Topology Term in Loss Function
- CFCT: A Framework Using ConvNeXt With FPN, CBAM and A Topology Enhanced Loss Function for Skin Lesion Segmentation
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