Li Su

2.7k total citations · 2 hit papers
57 papers, 1.8k citations indexed

About

Li Su is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Li Su has authored 57 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Computer Vision and Pattern Recognition, 14 papers in Artificial Intelligence and 9 papers in Signal Processing. Recurrent topics in Li Su's work include Visual Attention and Saliency Detection (12 papers), Image and Video Quality Assessment (12 papers) and Video Surveillance and Tracking Methods (11 papers). Li Su is often cited by papers focused on Visual Attention and Saliency Detection (12 papers), Image and Video Quality Assessment (12 papers) and Video Surveillance and Tracking Methods (11 papers). Li Su collaborates with scholars based in China, United States and Japan. Li Su's co-authors include Qingming Huang, Zhe Wu, Guorong Li, Yifan Yang, Zheng-Jun Zha, Shuhui Wang, Liang Li, Nicu Sebe, Weigang Zhang and Lei Miao and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Applied Energy and IEEE Transactions on Image Processing.

In The Last Decade

Li Su

56 papers receiving 1.8k 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.

Cascaded Partial Decoder for Fast and Accurate Salient Object Detection

2019 777 citations Zhe Wu, Li Su et al. profile →
Stacked Cross Refinement Network for Edge-Aware Salient Object Detection

2019 331 citations Zhe Wu, Li Su et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Li Su China	16	1.5k	325	255	187	150	57	1.8k
Miao Zhang China	25	1.7k 1.1×	134 0.4×	329 1.3×	358 1.9×	173 1.2×	69	2.2k
Jianwen Xie China	20	833 0.5×	337 1.0×	171 0.7×	94 0.5×	39 0.3×	59	1.4k
Lin Zheng China	14	954 0.6×	100 0.3×	164 0.6×	152 0.8×	87 0.6×	50	1.2k
Akihiro Sugimoto Japan	18	1.2k 0.8×	206 0.6×	63 0.2×	90 0.5×	65 0.4×	107	1.7k
Akshaya Mishra Canada	19	871 0.6×	78 0.2×	150 0.6×	191 1.0×	56 0.4×	67	2.0k
Chongyang Ma China	26	1.8k 1.1×	180 0.6×	152 0.6×	197 1.1×	10 0.1×	74	2.3k
Michael Donoser Austria	16	1000 0.6×	127 0.4×	53 0.2×	126 0.7×	42 0.3×	60	1.3k
Robinson Piramuthu United States	12	907 0.6×	424 1.3×	55 0.2×	115 0.6×	34 0.2×	26	1.3k

Countries citing papers authored by Li Su

Since Specialization

Citations

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

Fields of papers citing papers by Li Su

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Li Su

This figure shows the co-authorship network connecting the top 25 collaborators of Li Su. A scholar is included among the top collaborators of Li Su 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 Li Su. Li Su 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:

20 of 20 papers shown

1.

Chen, Junxi, et al.. (2024). Prompt-Enhanced Multiple Instance Learning for Weakly Supervised Video Anomaly Detection. 18319–18329. 13 indexed citations

2.

Tu, Yunbin, et al.. (2023). Viewpoint-Adaptive Representation Disentanglement Network for Change Captioning. IEEE Transactions on Image Processing. 32. 2620–2635. 16 indexed citations

3.

Tu, Yunbin, et al.. (2023). Neighborhood Contrastive Transformer for Change Captioning. IEEE Transactions on Multimedia. 25. 9518–9529. 9 indexed citations

4.

Zhang, Weigang, et al.. (2022). Temporal Dynamic Concept Modeling Network for Explainable Video Event Recognition. ACM Transactions on Multimedia Computing Communications and Applications. 19(6). 1–22. 1 indexed citations

5.

Li, Guorong, et al.. (2022). Self Supervised Progressive Network for High Performance Video Object Segmentation. IEEE Transactions on Neural Networks and Learning Systems. 35(6). 7671–7684. 4 indexed citations

6.

Zhang, Chen, Guorong Li, Qianqian Xu, et al.. (2022). Weakly Supervised Anomaly Detection in Videos Considering the Openness of Events. IEEE Transactions on Intelligent Transportation Systems. 23(11). 21687–21699. 21 indexed citations

7.

Liu, Jun, et al.. (2022). Online Activity Detection with Predicting and Classifying Transformers. 34. 1655–1660. 1 indexed citations

8.

Wang, Shuhui, et al.. (2021). Self-Regulated Learning for Egocentric Video Activity Anticipation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(6). 6715–6730. 32 indexed citations

9.

Wu, Zhe, Li Su, & Qingming Huang. (2021). Decomposition and Completion Network for Salient Object Detection. IEEE Transactions on Image Processing. 30. 6226–6239. 64 indexed citations

10.

Li, Liang, et al.. (2021). Rethinking Graph Neural Architecture Search from Message-passing. 6653–6662. 35 indexed citations

11.

Su, Li, et al.. (2020). Beat and Downbeat Tracking of Symbolic Music Data Using Deep Recurrent Neural Networks. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. 346–352. 3 indexed citations

12.

Yang, Yifan, Guorong Li, Zhe Wu, et al.. (2020). Reverse Perspective Network for Perspective-Aware Object Counting. Institutional Research Information System (Università degli Studi di Trento). 4373–4382. 102 indexed citations

13.

Yu, Hongyang, Guorong Li, Li Su, et al.. (2019). Conditional GAN based individual and global motion fusion for multiple object tracking in UAV videos. Pattern Recognition Letters. 131. 219–226. 20 indexed citations

14.

Zhang, Peng, Li Su, Liang Li, et al.. (2019). Training Efficient Saliency Prediction Models with Knowledge Distillation. 512–520. 15 indexed citations

15.

Wu, Zhe, Li Su, & Qingming Huang. (2019). Cascaded Partial Decoder for Fast and Accurate Salient Object Detection. 3902–3911. 777 indexed citations breakdown →

16.

Wu, Zhe, Li Su, & Qingming Huang. (2018). Learning Coupled Convolutional Networks Fusion for Video Saliency Prediction. IEEE Transactions on Circuits and Systems for Video Technology. 29(10). 2960–2971. 25 indexed citations

17.

Wu, Zhe, et al.. (2016). Video saliency prediction with optimized optical flow and gravity center bias. 1–6. 12 indexed citations

18.

Li, Yong, et al.. (2015). TCAM-efficient flow table mapping scheme for OpenFlow multiple-table pipelines. Journal of Tsinghua University(Science and Technology). 54(4). 437–442. 5 indexed citations

19.

Wang, Xi, Li Su, Qingming Huang, Chunxi Liu, & Ling‐Yu Duan. (2012). Motion Based Perceptual Distortion and Rate Optimization for Video Coding. 1061–1066. 2 indexed citations

20.

Su, Li, et al.. (2006). Translation-invariant wavelet de-noising method with improved thresholding. 1. 599–602. 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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