Bailan Feng

591 total citations
32 papers, 314 citations indexed

About

Bailan Feng is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Bailan Feng has authored 32 papers receiving a total of 314 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Computer Vision and Pattern Recognition, 9 papers in Signal Processing and 5 papers in Artificial Intelligence. Recurrent topics in Bailan Feng's work include Video Analysis and Summarization (11 papers), Advanced Image and Video Retrieval Techniques (11 papers) and Image Retrieval and Classification Techniques (10 papers). Bailan Feng is often cited by papers focused on Video Analysis and Summarization (11 papers), Advanced Image and Video Retrieval Techniques (11 papers) and Image Retrieval and Classification Techniques (10 papers). Bailan Feng collaborates with scholars based in China, Sweden and Hong Kong. Bailan Feng's co-authors include Bo Xu, Zhineng Chen, Jinfeng Bai, Kun Niu, Peng Yuan, Chen Zhu, Bing Yu, Qiang Zhao, Feng Dai and Yike Ma and has published in prestigious journals such as Neurocomputing, IEEE Transactions on Circuits and Systems for Video Technology and The Visual Computer.

In The Last Decade

Bailan Feng

32 papers receiving 303 citations

Peers

Bailan Feng
Hui Zeng China
Oliver Grau Germany
Lukas Bossard Switzerland
Congcong Li China
Yunbin Tu China
Jinhui Hu China
Hanlin Goh Singapore
Hui Zeng China
Bailan Feng
Citations per year, relative to Bailan Feng Bailan Feng (= 1×) peers Hui Zeng

Countries citing papers authored by Bailan Feng

Since Specialization
Citations

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

Fields of papers citing papers by Bailan Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bailan Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Bailan Feng. A scholar is included among the top collaborators of Bailan Feng 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 Bailan Feng. Bailan Feng 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.
Feng, Bailan, et al.. (2024). OctOcc: High-Resolution 3D Occupancy Prediction with Octree. Proceedings of the AAAI Conference on Artificial Intelligence. 38(5). 4369–4377. 4 indexed citations
2.
Wang, Zhongdao, et al.. (2024). SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction. 15035–15044. 17 indexed citations
3.
4.
Li, Zekun, et al.. (2022). SDTP: Semantic-Aware Decoupled Transformer Pyramid for Dense Image Prediction. IEEE Transactions on Circuits and Systems for Video Technology. 32(9). 6160–6173. 20 indexed citations
5.
Yuan, Peng, et al.. (2022). CF-DETR: Coarse-to-Fine Transformers for End-to-End Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence. 36(1). 185–193. 31 indexed citations
6.
Zhao, Qiang, et al.. (2021). Image stitching via deep homography estimation. Neurocomputing. 450. 219–229. 33 indexed citations
7.
8.
Li, Ke, et al.. (2018). Face Hallucination Based on Key Parts Enhancement. 1378–1382. 6 indexed citations
9.
Bare, Bahetiyaer, et al.. (2018). A Deep Learning Based No-Reference Image Quality Assessment Model for Single-Image Super-Resolution. 1223–1227. 18 indexed citations
10.
Yu, Bing, et al.. (2018). Knot Magnify Loss for Face Recognition. 2396–2400. 2 indexed citations
11.
Feng, Bailan, et al.. (2017). Hierarchical pedestrian attribute recognition based on adaptive region localization. 471–476. 6 indexed citations
12.
Chen, Zhineng, Bailan Feng, Chong‐Wah Ngo, Caiyan Jia, & Xiangsheng Huang. (2015). Improving Automatic Name-Face Association using Celebrity Images on the Web. 623–626. 3 indexed citations
13.
Bai, Jinfeng, Zhineng Chen, Bailan Feng, & Bo Xu. (2014). Image character recognition using deep convolutional neural network learned from different languages. 2560–2564. 50 indexed citations
14.
Xu, Su Xiu, Bailan Feng, Zhineng Chen, & Bo Xu. (2013). A general Framework of video segmentation to logical unit based on conditional random fields. 247–254. 8 indexed citations
15.
Xu, Su Xiu, Bailan Feng, Peng Ding, & Bo Xu. (2012). Graph-based multi-modal scene detection for movie and teleplay. 15. 1413–1416. 6 indexed citations
16.
Zhu, Lei, et al.. (2011). A robust approach to mining repeated sequence in audio stream. 2277–2280. 2 indexed citations
17.
Cao, Juan, et al.. (2010). Known-Item Search by MCG-ICT-CAS.. TRECVID. 1 indexed citations
18.
Feng, Bailan, Juan Cao, Zhineng Chen, Yongdong Zhang, & Shouxun Lin. (2010). Multi-modal query expansion for web video search. 721–722. 8 indexed citations
19.
Feng, Bailan, Juan Cao, Lei Bao, et al.. (2010). Graph-based multi-space semantic correlation propagation for video retrieval. The Visual Computer. 27(1). 21–34. 12 indexed citations
20.
Cao, Juan, Yongdong Zhang, Bailan Feng, et al.. (2009). TRECVID 2009 of MCG-ICT-CAS.. TRECVID. 4 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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2026