Longlong Jing

3.0k total citations · 1 hit paper
17 papers, 1.6k citations indexed

About

Longlong Jing is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Longlong Jing has authored 17 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 5 papers in Biomedical Engineering. Recurrent topics in Longlong Jing's work include Human Pose and Action Recognition (7 papers), Gait Recognition and Analysis (5 papers) and Multimodal Machine Learning Applications (4 papers). Longlong Jing is often cited by papers focused on Human Pose and Action Recognition (7 papers), Gait Recognition and Analysis (5 papers) and Multimodal Machine Learning Applications (4 papers). Longlong Jing collaborates with scholars based in United States, China and Germany. Longlong Jing's co-authors include Yingli Tian, Yucheng Chen, Jiaxing Tan, Xiaodong Yang, Zhe Wu, Yumei Huo, Hongcheng Wang, Lihong Li, Oğuz Akın and Toufiq Parag and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Language Resources and Evaluation.

In The Last Decade

Longlong Jing

16 papers receiving 1.6k 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.

Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey

2020 1.2k citations Longlong Jing, Yingli Tian IEEE Transactions on Pattern Analysis and Machine Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Longlong Jing United States	11	824	749	212	153	131	17	1.6k
Zichen Zhang China	11	858 1.0×	423 0.6×	188 0.9×	213 1.4×	138 1.1×	40	1.8k
Jia Deng China	5	1.2k 1.4×	778 1.0×	192 0.9×	135 0.9×	91 0.7×	14	1.9k
Gan Sun China	24	892 1.1×	925 1.2×	102 0.5×	119 0.8×	112 0.9×	68	1.7k
Maxime Oquab France	4	1.2k 1.4×	790 1.1×	194 0.9×	208 1.4×	113 0.9×	9	2.1k
Peng-Tao Jiang China	10	1.2k 1.5×	792 1.1×	226 1.1×	266 1.7×	153 1.2×	16	2.3k
Gang Yang China	19	1.1k 1.4×	593 0.8×	159 0.8×	272 1.8×	127 1.0×	79	2.2k
Teng Li China	22	1.1k 1.4×	743 1.0×	139 0.7×	170 1.1×	133 1.0×	139	2.2k
Zihang Jiang China	11	1.2k 1.5×	570 0.8×	156 0.7×	263 1.7×	93 0.7×	23	1.9k
Junqing Yu China	20	1.1k 1.4×	811 1.1×	192 0.9×	122 0.8×	140 1.1×	78	1.8k
Carl Doersch United States	10	1.5k 1.9×	954 1.3×	142 0.7×	193 1.3×	77 0.6×	13	2.2k

Countries citing papers authored by Longlong Jing

Since Specialization

Citations

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

Fields of papers citing papers by Longlong Jing

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Longlong Jing

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

17 of 17 papers shown

1.

Chen, Zhi‐Min, et al.. (2024). SAM-Guided Masked Token Prediction for 3D Scene Understanding. 82962–82981.

2.

Jing, Longlong, et al.. (2023). Multi-Modal Multi-Channel American Sign Language Recognition. 1(1). 3 indexed citations

3.

Chen, Zhimin, et al.. (2023). Class-Level Confidence Based 3D Semi-Supervised Learning. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 633–642. 8 indexed citations

4.

Jing, Longlong, Lin Zhang, Ju He, et al.. (2022). Learning from Temporal Gradient for Semi-supervised Action Recognition. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 3242–3252. 54 indexed citations

5.

Jing, Longlong, Ruichi Yu, Henrik Kretzschmar, et al.. (2022). Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking. 2022 International Conference on Robotics and Automation (ICRA). 11 indexed citations

6.

Jing, Longlong, et al.. (2022). Recognizing American Sign Language Manual Signs from Rgb-D Videos. SSRN Electronic Journal. 2 indexed citations

7.

Jing, Longlong, Toufiq Parag, Zhe Wu, Yingli Tian, & Hongcheng Wang. (2021). VideoSSL: Semi-Supervised Learning for Video Classification. 1109–1118. 48 indexed citations

8.

Jing, Longlong, et al.. (2021). Cross-Modal Center Loss for 3D Cross-Modal Retrieval. 3141–3150. 43 indexed citations

9.

Jing, Longlong, Ling Zhang, & Yingli Tian. (2021). Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences. 1581–1891. 21 indexed citations

10.

Jing, Longlong & Yingli Tian. (2020). Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(11). 4037–4058. 1200 indexed citations breakdown →

11.

Berke, Larwan, et al.. (2020). An Isolated-Signing RGBD Dataset of 100 American Sign Language Signs Produced by Fluent ASL Signers. Language Resources and Evaluation. 89–94. 3 indexed citations

12.

Tan, Jiaxing, Longlong Jing, Yumei Huo, et al.. (2020). LGAN: Lung segmentation in CT scans using generative adversarial network. Computerized Medical Imaging and Graphics. 87. 101817–101817. 59 indexed citations

13.

Jing, Longlong, Yucheng Chen, & Yingli Tian. (2019). Coarse-to-Fine Semantic Segmentation From Image-Level Labels. IEEE Transactions on Image Processing. 29. 225–236. 90 indexed citations

14.

Chen, Yu‐Cheng, et al.. (2019). Multi-camera Vehicle Tracking and Re-identification on AI City Challenge 2019. Computer Vision and Pattern Recognition. 324–332. 5 indexed citations

15.

Jing, Longlong & Yingli Tian. (2018). Self-supervised Spatiotemporal Feature Learning by Video Geometric Transformations. arXiv (Cornell University). 32 indexed citations

16.

Jing, Longlong, Xiaodong Yang, & Yingli Tian. (2018). Video you only look once: Overall temporal convolutions for action recognition. Journal of Visual Communication and Image Representation. 52. 58–65. 25 indexed citations

17.

Jing, Longlong, et al.. (2017). 3D convolutional neural network with multi-model framework for action recognition. abs 1609 3056. 1837–1841. 13 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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