Yalong Bai

1.3k total citations · 1 hit paper
27 papers, 655 citations indexed

About

Yalong Bai is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Social Psychology. According to data from OpenAlex, Yalong Bai has authored 27 papers receiving a total of 655 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 1 paper in Social Psychology. Recurrent topics in Yalong Bai's work include Advanced Image and Video Retrieval Techniques (10 papers), Multimodal Machine Learning Applications (10 papers) and Domain Adaptation and Few-Shot Learning (6 papers). Yalong Bai is often cited by papers focused on Advanced Image and Video Retrieval Techniques (10 papers), Multimodal Machine Learning Applications (10 papers) and Domain Adaptation and Few-Shot Learning (6 papers). Yalong Bai collaborates with scholars based in China, United States and United Kingdom. Yalong Bai's co-authors include Tao Mei, Wei Zhang, Yue Chen, Tiejun Zhao, Bin Gao, Tie‐Yan Liu, Jiang Bian, Gang Wang, Xiaoguang Liu and Chang Xu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Multimedia.

In The Last Decade

Yalong Bai

21 papers receiving 639 citations

Hit Papers

align trajectories sort by overperformance

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.

Destruction and Construction Learning for Fine-Grained Image Recognition

2019 356 citations Yue Chen, Yalong Bai et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yalong Bai China	9	457	354	57	28	26	27	655
Daniel Stanley Tan Taiwan	9	279 0.6×	170 0.5×	59 1.0×	13 0.5×	16 0.6×	37	460
Rameswar Debnath Bangladesh	11	184 0.4×	130 0.4×	35 0.6×	25 0.9×	14 0.5×	38	430
Ji Wan China	7	515 1.1×	189 0.5×	82 1.4×	9 0.3×	34 1.3×	8	676
Jin Yuan China	12	264 0.6×	107 0.3×	41 0.7×	12 0.4×	27 1.0×	31	383
Valentín Osuna-Enciso Mexico	11	204 0.4×	162 0.5×	84 1.5×	11 0.4×	23 0.9×	23	434
Mengde Xu China	4	341 0.7×	199 0.6×	52 0.9×	5 0.2×	35 1.3×	7	484
Carolina Reta Mexico	10	266 0.6×	114 0.3×	25 0.4×	21 0.8×	41 1.6×	17	370
Zhigang Dai China	5	298 0.7×	161 0.5×	67 1.2×	5 0.2×	49 1.9×	6	431
Xin Lai Hong Kong	9	296 0.6×	212 0.6×	40 0.7×	6 0.2×	29 1.1×	10	486

Countries citing papers authored by Yalong Bai

Since Specialization

Citations

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

Fields of papers citing papers by Yalong Bai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yalong Bai

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

All Works

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20 of 20 papers shown

Bai, Yalong, et al.. (2025). Interactive Conversational Head Generation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(8). 6673–6686.

Bai, Yalong, et al.. (2025). StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models. ACM Transactions on Multimedia Computing Communications and Applications. 21(5). 1–22.

Bai, Yalong, Ting Yao, Jingen Liu, et al.. (2024). Teaching Masked Autoencoder With Strong Augmentations. IEEE Transactions on Neural Networks and Learning Systems. 36(5). 9550–9564.

Zhang, Tianyu, et al.. (2024). Dynamic Prompt Optimizing for Text-to-Image Generation. 26617–26626. 3 indexed citations

Niu, Guanglin, et al.. (2024). CAMEL: CAusal Motion Enhancement Tailored for Lifting Text-Driven Video Editing. 9079–9088.

Bai, Yalong, et al.. (2023). Boosting Generic Visual-Linguistic Representation With Dynamic Contexts. IEEE Transactions on Multimedia. 25. 8445–8457. 1 indexed citations

Bai, Yalong, et al.. (2023). Visualizing and Understanding Patch Interactions in Vision Transformer. IEEE Transactions on Neural Networks and Learning Systems. 35(10). 13671–13680. 1 indexed citations

Bai, Yalong, et al.. (2023). Augmentation Pathways Network for Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(8). 10580–10587. 3 indexed citations

Bai, Yalong, Yifan Yang, Wei Zhang, & Tao Mei. (2022). Directional Self-supervised Learning for Heavy Image Augmentations. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 16671–16680. 17 indexed citations

10.

Bai, Yalong, et al.. (2021). Exploiting Relationship for Complex-scene Image Generation. Proceedings of the AAAI Conference on Artificial Intelligence. 35(2). 1584–1592. 9 indexed citations

11.

Zhang, Wei, et al.. (2021). Flat and Shallow: Understanding Fake Image Detection Models by Architecture Profiling. 1–5.

12.

Liang, Yuanzhi, Yalong Bai, Wei Zhang, et al.. (2019). Rethinking Visual Relationships for High-level Image Understanding.. arXiv (Cornell University). 2 indexed citations

13.

Chen, Yue, Yalong Bai, Wei Zhang, & Tao Mei. (2019). Destruction and Construction Learning for Fine-Grained Image Recognition. 5152–5161. 356 indexed citations breakdown →

14.

Liang, Yuanzhi, Yalong Bai, Wei Zhang, et al.. (2019). VrR-VG: Refocusing Visually-Relevant Relationships. 10402–10411. 40 indexed citations

15.

Yu, Wei, Kuiyuan Yang, Yalong Bai, Hongxun Yao, & Yong Rui. (2015). Learning Cross Space Mapping via DNN Using Large Scale Click-Through Logs. IEEE Transactions on Multimedia. 17(11). 2000–2007. 4 indexed citations

16.

Bai, Yalong, Kuiyuan Yang, Wei Yu, et al.. (2015). Automatic Image Dataset Construction from Click-through Logs Using Deep Neural Network. 441–450. 22 indexed citations

17.

Bai, Yalong, Wei Yu, Tianjun Xiao, et al.. (2014). Bag-of-Words Based Deep Neural Network for Image Retrieval. 229–232. 23 indexed citations

18.

Ma, Wei‐Ying, Tiejun Zhao, Kuiyuan Yang, Wei Yu, & Yalong Bai. (2013). Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data. arXiv (Cornell University). 1 indexed citations

19.

Yu, Mo, Tiejun Zhao, Yalong Bai, Hao Tian, & Dianhai Yu. (2013). Cross-lingual Projections between Languages from Different Families. Meeting of the Association for Computational Linguistics. 2. 312–317. 3 indexed citations

20.

Yu, Mo, Tiejun Zhao, & Yalong Bai. (2013). Learning domain differences automatically for dependency parsing adaptation. International Joint Conference on Artificial Intelligence. 1876–1882. 3 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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