Mengchen Liu

4.3k total citations · 3 hit papers
7 papers, 1.9k citations indexed

About

Mengchen Liu is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Mengchen Liu has authored 7 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 2 papers in Computational Mechanics and 2 papers in Artificial Intelligence. Recurrent topics in Mengchen Liu's work include Advanced Neural Network Applications (5 papers), Domain Adaptation and Few-Shot Learning (2 papers) and IoT and Edge/Fog Computing (1 paper). Mengchen Liu is often cited by papers focused on Advanced Neural Network Applications (5 papers), Domain Adaptation and Few-Shot Learning (2 papers) and IoT and Edge/Fog Computing (1 paper). Mengchen Liu collaborates with scholars based in United Kingdom, China and Germany. Mengchen Liu's co-authors include Lu Yuan, Dongdong Chen, Xiyang Dai, Yinpeng Chen, Zicheng Liu, Bin Xiao, Lei Zhang, Xiaoyi Dong, Jianlong Fu and Houwen Peng and has published in prestigious journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

In The Last Decade

Mengchen Liu

7 papers receiving 1.9k citations

Hit Papers

Dynamic Convolution: Attention Over Convolution Kernels 2020 2026 2022 2024 2020 2021 2022 250 500 750
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.

  1. Dynamic Convolution: Attention Over Convolution Kernels
    2020 788 citations Yinpeng Chen, Xiyang Dai et al. profile →
  2. Dynamic Head: Unifying Object Detection Heads with Attentions
    2021 551 citations Xiyang Dai, Yinpeng Chen et al. profile →
  3. Mobile-Former: Bridging MobileNet and Transformer
    2022 411 citations Yinpeng Chen, Xiyang Dai et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →

Peers

Mengchen Liu
Ningning Ma China
Xiaohan Ding China
Xuran Pan China
Xizhou Zhu China
Zhaowei Cai United States
Kaiwen Duan China
Zhixi Feng China
Zihang Jiang China
Ningning Ma China
Mengchen Liu
Citations per year, relative to Mengchen Liu Mengchen Liu (= 1×) peers Ningning Ma

Countries citing papers authored by Mengchen Liu

Since Specialization
Citations

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

Fields of papers citing papers by Mengchen Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mengchen Liu

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

All Works

7 of 7 papers shown
1.
Chen, Yinpeng, Xiyang Dai, Dongdong Chen, et al.. (2022). Mobile-Former: Bridging MobileNet and Transformer. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5260–5269. 411 indexed citations breakdown →
2.
Zhang, Jinnian, Houwen Peng, Kan Wu, et al.. (2022). MiniViT: Compressing Vision Transformers with Weight Multiplexing. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 12135–12144. 79 indexed citations
3.
Liu, Qiankun, Zhentao Tan, Dongdong Chen, et al.. (2022). Reduce Information Loss in Transformers for Pluralistic Image Inpainting. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 11337–11347. 65 indexed citations
4.
Li, Yunsheng, Yinpeng Chen, Xiyang Dai, et al.. (2021). MicroNet: Improving Image Recognition with Extremely Low FLOPs. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 458–467. 53 indexed citations
5.
Dai, Xiyang, Yinpeng Chen, Bin Xiao, et al.. (2021). Dynamic Head: Unifying Object Detection Heads with Attentions. 7369–7378. 551 indexed citations breakdown →
6.
Chen, Yinpeng, Xiyang Dai, Mengchen Liu, et al.. (2020). Dynamic Convolution: Attention Over Convolution Kernels. 11027–11036. 788 indexed citations breakdown →
7.
Song, Jeungeun, et al.. (2018). Emotion Communications Driven by Big Data Cloud, Smart Clothing and Robotics. 2 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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