Fa-Ting Hong

842 total citations · 1 hit paper
14 papers, 460 citations indexed

About

Fa-Ting Hong is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Fa-Ting Hong has authored 14 papers receiving a total of 460 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 2 papers in Signal Processing. Recurrent topics in Fa-Ting Hong's work include Human Pose and Action Recognition (7 papers), Multimodal Machine Learning Applications (6 papers) and Anomaly Detection Techniques and Applications (4 papers). Fa-Ting Hong is often cited by papers focused on Human Pose and Action Recognition (7 papers), Multimodal Machine Learning Applications (6 papers) and Anomaly Detection Techniques and Applications (4 papers). Fa-Ting Hong collaborates with scholars based in China, Hong Kong and United Kingdom. Fa-Ting Hong's co-authors include Wei‐Shi Zheng, Dan Xu, Li Shen, Ying Shan, Weihong Li, Jianhuang Lai, Wei Zeng, Qizhi Yu, Yaowei Wang and Weihong Li and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Circuits and Systems for Video Technology and IEEE Transactions on Multimedia.

In The Last Decade

Fa-Ting Hong

12 papers receiving 449 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.

MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection

2021 209 citations Fa-Ting Hong, Wei‐Shi Zheng et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fa-Ting Hong China	7	314	278	146	99	43	14	460
Alexei Gritai United States	5	314 1.0×	241 0.9×	82 0.6×	88 0.9×	55 1.3×	8	403
Oluwatoyin P. Popoola Nigeria	4	290 0.9×	275 1.0×	94 0.6×	69 0.7×	39 0.9×	10	379
Davide Abati Italy	4	106 0.3×	294 1.1×	146 1.0×	63 0.6×	31 0.7×	6	327
Muhammad Zaigham Zaheer United Arab Emirates	12	194 0.6×	343 1.2×	234 1.6×	97 1.0×	46 1.1×	31	427
Jathushan Rajasegaran Australia	9	209 0.7×	141 0.5×	21 0.1×	53 0.5×	19 0.4×	13	319
Haichao Shi China	12	231 0.7×	206 0.7×	36 0.2×	42 0.4×	32 0.7×	25	347
Romero Morais Australia	4	127 0.4×	232 0.8×	113 0.8×	50 0.5×	15 0.3×	6	261
Manassés Ribeiro Brazil	5	129 0.4×	194 0.7×	88 0.6×	37 0.4×	20 0.5×	12	268
K. Deepak India	9	134 0.4×	190 0.7×	119 0.8×	46 0.5×	20 0.5×	24	294
Xueyi Wang Netherlands	6	186 0.6×	59 0.2×	45 0.3×	126 1.3×	21 0.5×	10	298

Countries citing papers authored by Fa-Ting Hong

Since Specialization

Citations

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

Fields of papers citing papers by Fa-Ting Hong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fa-Ting Hong. 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 Fa-Ting Hong. The network helps show where Fa-Ting Hong may publish in the future.

Co-authorship network of co-authors of Fa-Ting Hong

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

14 of 14 papers shown

1.

zhou, ZIQING, Jun Zhou, Xiaoyu Jin, et al.. (2025). HunyuanPortrait: Implicit Condition Control for Enhanced Portrait Animation. 15909–15919. 1 indexed citations

2.

Li, Hanhui, et al.. (2025). FireEdit: Fine-grained Instruction-based Image Editing via Region-aware Vision Language Model. 13093–13103. 1 indexed citations

3.

Hong, Fa-Ting, et al.. (2025). Synergizing Motion and Appearance: Multi-Scale Compensatory Codebooks for Talking Head Video Generation. 26232–26241.

4.

Wang, Yu, et al.. (2025). AnyTalk: Multi-modal Driven Multi-domain Talking Head Generation. Proceedings of the AAAI Conference on Artificial Intelligence. 39(8). 8105–8113.

5.

Lin, Kun-Yu, Fa-Ting Hong, Zhongang Qi, et al.. (2024). Weakly-Supervised Temporal Action Localization by Progressive Complementary Learning. IEEE Transactions on Circuits and Systems for Video Technology. 35(1). 938–952. 2 indexed citations

6.

Hong, Fa-Ting, Li Shen, & Dan Xu. (2023). DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(5). 2997–3012. 5 indexed citations

7.

Hong, Fa-Ting & Dan Xu. (2023). Implicit Identity Representation Conditioned Memory Compensation Network for Talking Head Video Generation. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 23005–23015. 10 indexed citations

8.

Hong, Fa-Ting, et al.. (2022). Learning Relation Models to Detect Important People in Still Images. IEEE Transactions on Multimedia. 25. 6601–6615. 1 indexed citations

9.

Hong, Fa-Ting, et al.. (2022). Depth-Aware Generative Adversarial Network for Talking Head Video Generation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 3387–3396. 90 indexed citations

10.

Hong, Fa-Ting, et al.. (2021). MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection. 14004–14013. 209 indexed citations breakdown →

11.

Hong, Fa-Ting, et al.. (2021). Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1591–1599. 67 indexed citations

12.

Hong, Fa-Ting, Wei‐Shi Zheng, Qizhi Yu, et al.. (2020). Hybrid Dynamic-static Context-aware Attention Network for Action Assessment in Long Videos. 2526–2534. 40 indexed citations

13.

Hong, Fa-Ting, Weihong Li, & Wei‐Shi Zheng. (2020). Learning to Detect Important People in Unlabelled Images for Semi-Supervised Important People Detection. 4145–4153. 14 indexed citations

14.

Li, Weihong, Fa-Ting Hong, & Wei‐Shi Zheng. (2019). Learning to Learn Relation for Important People Detection in Still Images. 4998–5006. 20 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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