Helong Zhou

713 total citations · 1 hit paper
6 papers, 298 citations indexed

About

Helong Zhou is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Automotive Engineering. According to data from OpenAlex, Helong Zhou has authored 6 papers receiving a total of 298 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 1 paper in Automotive Engineering. Recurrent topics in Helong Zhou's work include Advanced Neural Network Applications (5 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Advanced Image and Video Retrieval Techniques (3 papers). Helong Zhou is often cited by papers focused on Advanced Neural Network Applications (5 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Advanced Image and Video Retrieval Techniques (3 papers). Helong Zhou collaborates with scholars based in China, Taiwan and United States. Helong Zhou's co-authors include Zhulin An, Chuanguang Yang, Yongjun Xu, Xue Jiang, Qian Zhang, Qian Zhang, Shaoyu Chen, Qian Zhang, Bo Jiang and Chang Huang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

In The Last Decade

Helong Zhou

6 papers receiving 292 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Cross-Image Relational Knowledge Distillation for Semantic Segmentation

2022 141 citations Chuanguang Yang, Helong Zhou et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Helong Zhou China	4	152	127	41	37	21	6	298
Chongjian Ge Hong Kong	8	264 1.7×	74 0.6×	15 0.4×	30 0.8×	22 1.0×	9	350
Mengshi Qi China	12	453 3.0×	225 1.8×	16 0.4×	28 0.8×	25 1.2×	32	543
Abdessalem Ben Abdelali Tunisia	10	177 1.2×	40 0.3×	21 0.5×	25 0.7×	35 1.7×	41	280
Adnan Ahmed Rafique Pakistan	10	353 2.3×	101 0.8×	11 0.3×	32 0.9×	50 2.4×	15	481
Abrar Ahmed Pakistan	7	422 2.8×	106 0.8×	12 0.3×	31 0.8×	50 2.4×	9	537
Songhao Zhu China	10	316 2.1×	105 0.8×	43 1.0×	11 0.3×	37 1.8×	104	422
Rana Hammad Raza Pakistan	8	186 1.2×	102 0.8×	10 0.2×	11 0.3×	20 1.0×	25	269
Haigang Zhang China	9	160 1.1×	113 0.9×	61 1.5×	7 0.2×	16 0.8×	22	376
Qazi Mazhar ul Haq Taiwan	8	84 0.6×	59 0.5×	20 0.5×	10 0.3×	8 0.4×	27	228
Mohammad Kazem Moghimi Iran	10	290 1.9×	178 1.4×	22 0.5×	9 0.2×	46 2.2×	21	450

Countries citing papers authored by Helong Zhou

Since Specialization

Citations

This map shows the geographic impact of Helong Zhou'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 Helong Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Helong Zhou more than expected).

Fields of papers citing papers by Helong Zhou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Helong Zhou. 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 Helong Zhou. The network helps show where Helong Zhou may publish in the future.

Co-authorship network of co-authors of Helong Zhou

This figure shows the co-authorship network connecting the top 25 collaborators of Helong Zhou. A scholar is included among the top collaborators of Helong Zhou 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 Helong Zhou. Helong Zhou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

6 of 6 papers shown

1.

Yang, Chuanguang, Zhulin An, Helong Zhou, et al.. (2023). Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(8). 10212–10227. 48 indexed citations

2.

Song, Liangchen, et al.. (2023). Exploring the Knowledge Transferred by Response-Based Teacher-Student Distillation. 2704–2713. 2 indexed citations

3.

Jiang, Bo, Shaoyu Chen, Qing Xu, et al.. (2023). VAD: Vectorized Scene Representation for Efficient Autonomous Driving. 8306–8316. 77 indexed citations

4.

Chen, Pei-yu, et al.. (2022). Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 4491–4503. 29 indexed citations

5.

Yang, Chuanguang, Helong Zhou, Zhulin An, et al.. (2022). Cross-Image Relational Knowledge Distillation for Semantic Segmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 12309–12318. 141 indexed citations breakdown →

6.

Zhou, Helong, Yie‐Tarng Chen, Jie Zhang, & Wen‐Hsien Fang. (2018). Efficient Weighted Kernel Sharing Convolutional Neural Networks. 323. 1–4. 1 indexed citations

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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