Liangchen Song

2.0k total citations · 2 hit papers
22 papers, 1.2k citations indexed

About

Liangchen Song is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Graphics and Computer-Aided Design. According to data from OpenAlex, Liangchen Song has authored 22 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 7 papers in Artificial Intelligence and 4 papers in Computer Graphics and Computer-Aided Design. Recurrent topics in Liangchen Song's work include Human Pose and Action Recognition (7 papers), Advanced Neural Network Applications (7 papers) and Video Surveillance and Tracking Methods (6 papers). Liangchen Song is often cited by papers focused on Human Pose and Action Recognition (7 papers), Advanced Neural Network Applications (7 papers) and Video Surveillance and Tracking Methods (6 papers). Liangchen Song collaborates with scholars based in United States, China and Germany. Liangchen Song's co-authors include Junsong Yuan, Lefei Zhang, Jialian Wu, Bo Du, Yipeng Zhang, Xinggang Wang, Ming Yang, Yu Wang, Jiale Cao and Cheng Wang and has published in prestigious journals such as IEEE Transactions on Image Processing, IEEE Transactions on Medical Imaging and Pattern Recognition.

In The Last Decade

Liangchen Song

21 papers receiving 1.1k citations

Hit Papers

Track to Detect and Segment: An Online Multi-Object Tracker 2021 2026 2022 2024 2021 2023 50 100 150 200
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. Track to Detect and Segment: An Online Multi-Object Tracker
    2021 245 citations Jialian Wu, Jiale Cao et al. profile →
  2. NeRFPlayer: A Streamable Dynamic Scene Representation with Decomposed Neural Radiance Fields
    2023 123 citations Liangchen Song, Zhong Li et al. profile →

Peers

Liangchen Song
Yang Wu China
Wenxiu Sun Hong Kong
Avinash Ravichandran United States
Seungryong Kim South Korea
Anurag Ranjan Germany
Antoine Vacavant France
Hehe Fan China
Xianbiao Qi China
Jun Hao Liew Singapore
Yang Wu China
Liangchen Song
Citations per year, relative to Liangchen Song Liangchen Song (= 1×) peers Yang Wu

Countries citing papers authored by Liangchen Song

Since Specialization
Citations

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

Fields of papers citing papers by Liangchen Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liangchen Song

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

All Works

20 of 20 papers shown
1.
Song, Liangchen, et al.. (2023). Exploring the Knowledge Transferred by Response-Based Teacher-Student Distillation. 2704–2713. 2 indexed citations
2.
Gong, Xuan, Liangchen Song, Meng Zheng, et al.. (2023). Progressive Multi-View Human Mesh Recovery with Self-Supervision. Proceedings of the AAAI Conference on Artificial Intelligence. 37(1). 676–684. 4 indexed citations
3.
Li, Zhong, Liangchen Song, Zhang Chen, et al.. (2023). Relit-NeuLF: Efficient Relighting and Novel View Synthesis via Neural 4D Light Field. 7007–7016. 5 indexed citations
4.
Chen, Zhang, Zhong Li, Liangchen Song, et al.. (2023). NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions. 4159–4171. 35 indexed citations
5.
Song, Liangchen, et al.. (2023). RoomDreamer: Text-Driven 3D Indoor Scene Synthesis with Coherent Geometry and Texture. 6898–6906. 13 indexed citations
6.
Gong, Xuan, Liangchen Song, Abhishek Sharma, et al.. (2022). Federated Learning With Privacy-Preserving Ensemble Attention Distillation. IEEE Transactions on Medical Imaging. 42(7). 2057–2067. 26 indexed citations
7.
Song, Liangchen, Gang Yu, Junsong Yuan, & Zicheng Liu. (2021). Human pose estimation and its application to action recognition: A survey. Journal of Visual Communication and Image Representation. 76. 103055–103055. 106 indexed citations
8.
Song, Liangchen, et al.. (2021). Stacked Homography Transformations for Multi-View Pedestrian Detection. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 6029–6037. 27 indexed citations
9.
Song, Liangchen, Jialian Wu, Ming Yang, et al.. (2021). Handling Difficult Labels for Multi-label Image Classification via Uncertainty Distillation. 2410–2419. 10 indexed citations
10.
Wu, Jialian, Liangchen Song, Qian Zhang, Ming Yang, & Junsong Yuan. (2021). ForestDet: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation. IEEE Transactions on Multimedia. 24. 3693–3705. 9 indexed citations
11.
Song, Liangchen, et al.. (2021). Learning Kinematic Formulas from Multiple View Videos. 126–134. 1 indexed citations
12.
Liu, Sheng, Liangchen Song, Yi Xu, & Junsong Yuan. (2021). NeCH: Neural Clothed Human Model. 1–5.
13.
Wu, Jialian, Jiale Cao, Liangchen Song, et al.. (2021). Track to Detect and Segment: An Online Multi-Object Tracker. 12347–12356. 245 indexed citations breakdown →
14.
Song, Liangchen, Jialian Wu, Ming Yang, et al.. (2021). Robust Knowledge Transfer via Hybrid Forward on the Teacher-Student Model. Proceedings of the AAAI Conference on Artificial Intelligence. 35(3). 2558–2566. 6 indexed citations
15.
Wu, Jialian, Liangchen Song, Tiancai Wang, Qian Zhang, & Junsong Yuan. (2020). Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation. 1570–1578. 55 indexed citations
16.
Song, Liangchen, Cheng Wang, Lefei Zhang, et al.. (2020). Unsupervised domain adaptive re-identification: Theory and practice. Pattern Recognition. 102. 107173–107173. 237 indexed citations
17.
Zhang, Lefei, Liangchen Song, Bo Du, & Yipeng Zhang. (2019). Nonlocal Low-Rank Tensor Completion for Visual Data. IEEE Transactions on Cybernetics. 51(2). 673–685. 195 indexed citations
18.
Fu, Chaoyou, Liangchen Song, Xiang Wu, Guoli Wang, & Ran He. (2019). Neurons Merging Layer: Towards Progressive Redundancy Reduction for Deep Supervised Hashing. 2322–2328. 5 indexed citations
19.
Wang, Cheng, et al.. (2019). Multi-scale multi-patch person re-identification with exclusivity regularized softmax. Neurocomputing. 382. 64–70. 24 indexed citations
20.
Song, Liangchen, Bo Du, Lefei Zhang, et al.. (2018). Nonlocal Patch Based t-SVD for Image Inpainting: Algorithm and Error Analysis. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 9 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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