Sihai Tang

1.1k total citations
17 papers, 704 citations indexed

About

Sihai Tang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Sihai Tang has authored 17 papers receiving a total of 704 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 7 papers in Computer Networks and Communications. Recurrent topics in Sihai Tang's work include Advanced Neural Network Applications (11 papers), Domain Adaptation and Few-Shot Learning (5 papers) and IoT and Edge/Fog Computing (4 papers). Sihai Tang is often cited by papers focused on Advanced Neural Network Applications (11 papers), Domain Adaptation and Few-Shot Learning (5 papers) and IoT and Edge/Fog Computing (4 papers). Sihai Tang collaborates with scholars based in United States and China. Sihai Tang's co-authors include Song Fu, Qing Yang, Qi Chen, Jingda Guo, Xu Ma, Qing Yang, Qi Chen, Qing Yang, Paparao Palacharla and Qi Chen and has published in prestigious journals such as IEEE Internet of Things Journal, IEEE Transactions on Multimedia and Algorithms.

In The Last Decade

Sihai Tang

17 papers receiving 696 citations

Peers

Countries citing papers authored by Sihai Tang

Since Specialization

Citations

This map shows the geographic impact of Sihai Tang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sihai Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sihai Tang more than expected).

Fields of papers citing papers by Sihai Tang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sihai Tang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sihai Tang. The network helps show where Sihai Tang may publish in the future.

Co-authorship network of co-authors of Sihai Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Sihai Tang. A scholar is included among the top collaborators of Sihai Tang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sihai Tang. Sihai Tang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

#	Work	Indexed citations
1	Characterizing Perception Deep Learning Algorithms and Applications for Vehicular Edge Computing 2025 ·Algorithms ·(unknown), Sihai Tang, (unknown), Ying He, (unknown), Qing Yang, Song Fu	1
2	MLCNN: Cross-Layer Cooperative Optimization and Accelerator Architecture for Speeding Up Deep Learning Applications 2022 ·(unknown), (unknown), Sihai Tang, Xu Ma, (unknown), Song Fu, Qing Yang, M. X. Liu	1
3	Spatial Pyramid Attention for Deep Convolutional Neural Networks 2021 ·IEEE Transactions on Multimedia ·Xu Ma, Jingda Guo, (unknown), (unknown), (unknown), Qi Chen, Sihai Tang, Qing Yang, Song Fu	32
4	Vehicular Edge Computing for Multi-Vehicle Perception 2021 ·Sihai Tang, (unknown), Song Fu, Qing Yang	8
5	CoFF: Cooperative Spatial Feature Fusion for 3-D Object Detection on Autonomous Vehicles 2021 ·IEEE Internet of Things Journal ·Jingda Guo, (unknown), Sihai Tang, Qi Chen, Qing Yang, Song Fu, (unknown), Nannan Wang, Paparao Palacharla	42
6	VECFrame: A Vehicular Edge Computing Framework for Connected Autonomous Vehicles 2021 ·Sihai Tang, (unknown), (unknown), (unknown), Song Fu, Qing Yang, Paparao Palacharla, Nannan Wang, Xi Wang, Weisong Shi	7
7	Characterizing Job-Task Dependency in Cloud Workloads Using Graph Learning 2021 ·(unknown), Sihai Tang, (unknown), (unknown), Qiang Guan, Song Fu	3
8	Learning Connected Attentions for Convolutional Neural Networks 2021 ·Xu Ma, Jingda Guo, Sihai Tang, (unknown), Qi Chen, Qing Yang, Song Fu, Paparao Palacharla, Nannan Wang, Xi Wang	18
9	Spanet: Spatial Pyramid Attention Network for Enhanced Image Recognition 2020 ·Jingda Guo, Xu Ma, (unknown), (unknown), (unknown), Qi Chen, Sihai Tang, Qing Yang, Song Fu	61
10	Attention Meets Normalization and Beyond 2020 ·Xu Ma, Jingda Guo, Qi Chen, Sihai Tang, Qing Yang, Song Fu	2
11	A middle-ware approach to leverage the distributed data de-duplication capability on HPC and Cloud storage systems 2020 ·(unknown), Sihai Tang, Song Fu	1
12	Cascaded Context Dependency: An Extremely Lightweight Module For Deep Convolutional Neural Networks 2020 ·Xu Ma, (unknown), Jingda Guo, Sihai Tang, Qi Chen, Qing Yang, Song Fu	1
13	Cooperative Mixed Reality Leveraging Edge Computing and Communication 2020 ·Sihai Tang, (unknown), (unknown), (unknown), Song Fu	2
14	Cooper: Cooperative Perception for Connected Autonomous Vehicles Based on 3D Point Clouds 2019 ·Qi Chen, Sihai Tang, Qing Yang, Song Fu	249
15	An Empirical Study of Quad-Level Cell (QLC) NAND Flash SSDs for Big Data Applications 2019 ·(unknown), (unknown), Sihai Tang, (unknown), Song Fu, Weisong Shi, (unknown)	17
16	F-cooper 2019 ·Qi Chen, Xu Ma, Sihai Tang, Jingda Guo, Qing Yang, Song Fu	244
17	Smart Home IoT Anomaly Detection based on Ensemble Model Learning From Heterogeneous Data 2019 ·Sihai Tang, (unknown), Qing Yang, Song Fu	15

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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