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.
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning
2019358 citationsShaohui Lin, Rongrong Ji et al.profile →
CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
2020340 citationsYuge Huang, Ying Tai et al.profile →
Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
2019292 citationsFeng Zheng, Xing Sun et al.profile →
Dual-level Collaborative Transformer for Image Captioning
2021216 citationsJiayi Ji, Xiaoshuai Sun et al.profile →
Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework
2021198 citationsQingyu Song, Changan Wang et al.2021 IEEE/CVF International Conference on Computer Vision (ICCV)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Feiyue Huang'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 Feiyue Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Feiyue Huang more than expected).
This network shows the impact of papers produced by Feiyue Huang. 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 Feiyue Huang. The network helps show where Feiyue Huang may publish in the future.
Co-authorship network of co-authors of Feiyue Huang
This figure shows the co-authorship network connecting the top 25 collaborators of Feiyue Huang.
A scholar is included among the top collaborators of Feiyue Huang 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 Feiyue Huang. Feiyue Huang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Huang, Yuge, Pengcheng Shen, Ying Tai, et al.. (2020). Distribution Distillation Loss: Generic Approach for Improving Face Recognition from Hard Samples. arXiv (Cornell University).1 indexed citations
9.
Li, Yuchao, Rongrong Ji, Shaohui Lin, et al.. (2019). Dynamic Neural Network Decoupling.. arXiv (Cornell University).3 indexed citations
Chen, Fuhai, Rongrong Ji, Jiayi Ji, et al.. (2019). Variational Structured Semantic Inference for Diverse Image Captioning. Neural Information Processing Systems. 32. 1929–1939.13 indexed citations
Zheng, Feng, et al.. (2018). A Coarse-to-fine Pyramidal Model for Person Re-identification via Multi-Loss Dynamic Training.. arXiv (Cornell University).8 indexed citations
Tang, Fan, Weiming Dong, Xing Mei, et al.. (2017). Animated Construction of Chinese Brush Paintings. IEEE Transactions on Visualization and Computer Graphics. 24(12). 3019–3031.19 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.