Hanwang Zhang

28.1k total citations · 11 hit papers
204 papers, 11.8k citations indexed

About

Hanwang Zhang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Reproductive Medicine. According to data from OpenAlex, Hanwang Zhang has authored 204 papers receiving a total of 11.8k indexed citations (citations by other indexed papers that have themselves been cited), including 137 papers in Computer Vision and Pattern Recognition, 96 papers in Artificial Intelligence and 24 papers in Reproductive Medicine. Recurrent topics in Hanwang Zhang's work include Multimodal Machine Learning Applications (80 papers), Domain Adaptation and Few-Shot Learning (59 papers) and Advanced Image and Video Retrieval Techniques (57 papers). Hanwang Zhang is often cited by papers focused on Multimodal Machine Learning Applications (80 papers), Domain Adaptation and Few-Shot Learning (59 papers) and Advanced Image and Video Retrieval Techniques (57 papers). Hanwang Zhang collaborates with scholars based in China, Singapore and United States. Hanwang Zhang's co-authors include Tat‐Seng Chua, Xiangnan He, Wei Liu, Jun Xiao, Kaihua Tang, Liqiang Nie, Long Chen, Yulei Niu, Xu Yang and Jianfei Cai and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, The Journal of Clinical Endocrinology & Metabolism and Scientific Reports.

In The Last Decade

Hanwang Zhang

193 papers receiving 11.6k citations

Hit Papers

SCA-CNN: Spatial and Channel-Wise Attention in Convolutio... 2016 2026 2019 2022 2017 2016 2017 2019 2017 400 800 1.2k
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.

  1. SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning
    2017 1.4k citations Long Chen, Hanwang Zhang et al. profile →
  2. Fast Matrix Factorization for Online Recommendation with Implicit Feedback
    2016 660 citations Xiangnan He, Hanwang Zhang et al. profile →
  3. Attentive Collaborative Filtering
    2017 571 citations Hanwang Zhang, Xiangnan He et al. profile →
  4. Auto-Encoding Scene Graphs for Image Captioning
    2019 530 citations Xu Yang, Kaihua Tang et al. profile →
  5. Video Captioning With Attention-Based LSTM and Semantic Consistency
    2017 478 citations Hanwang Zhang et al. IEEE Transactions on Multimedia profile →
  6. Unbiased Scene Graph Generation From Biased Training
    2020 429 citations Kaihua Tang, Yulei Niu et al. profile →
  7. Learning to Compose Dynamic Tree Structures for Visual Contexts
    2019 332 citations Kaihua Tang, Hanwang Zhang et al. profile →
  8. Attributed Social Network Embedding
    2018 322 citations Xiangnan He, Hanwang Zhang et al. profile →
  9. Counterfactual VQA: A Cause-Effect Look at Language Bias
    2021 266 citations Yulei Niu, Kaihua Tang et al. profile →
  10. Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation
    2022 138 citations Tan Wang, Xian‐Sheng Hua et al. profile →
  11. Prompt-aligned Gradient for Prompt Tuning
    2023 119 citations Yulei Niu, Hanwang Zhang et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hanwang Zhang China 51 7.9k 5.9k 1.7k 434 413 204 11.8k
Weiming Hu China 51 8.8k 1.1× 3.0k 0.5× 285 0.2× 701 1.6× 302 0.7× 306 11.5k
Sanghamitra Bandyopadhyay India 45 2.0k 0.3× 5.2k 0.9× 917 0.5× 591 1.4× 393 1.0× 239 10.3k
Rong Jin United States 53 4.8k 0.6× 5.4k 0.9× 1.3k 0.8× 559 1.3× 611 1.5× 274 9.6k
Yong Rui China 55 11.1k 1.4× 3.4k 0.6× 659 0.4× 1.0k 2.3× 262 0.6× 203 13.3k
Shuai Liu China 41 2.0k 0.3× 2.3k 0.4× 694 0.4× 410 0.9× 289 0.7× 235 5.6k
Loris Nanni Italy 45 3.0k 0.4× 1.7k 0.3× 726 0.4× 551 1.3× 161 0.4× 260 7.3k
Chen Ding China 20 2.4k 0.3× 3.4k 0.6× 1.2k 0.7× 535 1.2× 586 1.4× 125 9.0k
Xiaochun Cao China 60 10.4k 1.3× 4.1k 0.7× 437 0.3× 2.8k 6.3× 274 0.7× 426 15.3k
Gai‐Ge Wang China 62 1.7k 0.2× 7.3k 1.2× 623 0.4× 227 0.5× 1.5k 3.6× 189 12.9k
Yudong Yao United States 54 1.2k 0.2× 3.0k 0.5× 181 0.1× 309 0.7× 3.9k 9.5× 383 10.5k

Countries citing papers authored by Hanwang Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Hanwang Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hanwang Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Hanwang Zhang. A scholar is included among the top collaborators of Hanwang Zhang 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 Hanwang Zhang. Hanwang Zhang 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.
Dong, Yinpeng, et al.. (2025). Align Is Not Enough: Multimodal Universal Jailbreak Attack Against Multimodal Large Language Models. IEEE Transactions on Circuits and Systems for Video Technology. 35(6). 5475–5488.
2.
Dong, Yinpeng, et al.. (2025). Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-Training Models With Contrastive Learning. IEEE Transactions on Multimedia. 27. 6410–6421. 1 indexed citations
3.
Zhang, Chi, Xin Chen, Xu Yang, et al.. (2025). A Multimodal LLM for Chart Understanding and Generation. 1–8.
4.
Du, Xiaoyu, et al.. (2024). MGNet: Learning Correspondences via Multiple Graphs. Proceedings of the AAAI Conference on Artificial Intelligence. 38(4). 3945–3953. 2 indexed citations
5.
Zhang, Hanwang, et al.. (2024). MiR-450b-5p enhances the radiosensitivity of HR+ and HER2− breast cancer by targeting CDK6. Oncology and Translational Medicine. 10(4). 198–203. 1 indexed citations
6.
Xiao, Jun, et al.. (2024). NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(10). 6873–6888. 7 indexed citations
7.
Zhang, Dong, et al.. (2024). Fine-Tuning for Few-Shot Image Classification by Multimodal Prototype Regularization. IEEE Transactions on Multimedia. 26. 8543–8556. 5 indexed citations
8.
Yue, Zhongqi, Tan Wang, Qianru Sun, Xian‐Sheng Hua, & Hanwang Zhang. (2021). Counterfactual Zero-Shot and Open-Set Visual Recognition. 15399–15409. 133 indexed citations
9.
Qin, Wei, Hanwang Zhang, Richang Hong, Ee‐Peng Lim, & Qianru Sun. (2021). Causal Interventional Training for Image Recognition. IEEE Transactions on Multimedia. 25. 1033–1044. 23 indexed citations
10.
Tang, Kaihua, Yulei Niu, Jianqiang Huang, Jiaxin Shi, & Hanwang Zhang. (2020). Unbiased Scene Graph Generation From Biased Training. 3713–3722. 429 indexed citations breakdown →
11.
He, Yang, Yuhang Ding, Ping Liu, et al.. (2020). Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration. 2006–2015. 149 indexed citations
12.
Yue, Zhongqi, Hanwang Zhang, Qianru Sun, & Xian‐Sheng Hua. (2020). Interventional few-shot learning. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 33. 2734–2746. 19 indexed citations
13.
Wang, Meng, et al.. (2020). More Grounded Image Captioning by Distilling Image-Text Matching Model. 4776–4785. 91 indexed citations
14.
Yang, Xu, Hanwang Zhang, & Jianfei Cai. (2019). Learning to Collocate Neural Modules for Image Captioning. 4249–4259. 57 indexed citations
15.
Yang, Tianhao, Zheng-Jun Zha, & Hanwang Zhang. (2019). Making History Matter: History-Advantage Sequence Training for Visual Dialog. 2561–2569. 36 indexed citations
16.
Xu, Ning, Hanwang Zhang, An-An Liu, et al.. (2019). Multi-Level Policy and Reward-Based Deep Reinforcement Learning Framework for Image Captioning. IEEE Transactions on Multimedia. 22(5). 1372–1383. 84 indexed citations
17.
Yang, Tianhao, Zheng-Jun Zha, & Hanwang Zhang. (2019). Making History Matter: Gold-Critic Sequence Training for Visual Dialog.. arXiv (Cornell University). 5 indexed citations
18.
Yang, Xu, Kaihua Tang, Hanwang Zhang, & Jianfei Cai. (2018). Auto-Encoding Graphical Inductive Bias for Descriptive Image Captioning. arXiv (Cornell University). 1 indexed citations
19.
Chen, Long, Hanwang Zhang, Jun Xiao, et al.. (2018). Scene Dynamics: Counterfactual Critic Multi-Agent Training for Scene Graph Generation.. arXiv (Cornell University). 7 indexed citations
20.
Liao, Shujie, Weina Zhang, Wei Wang, et al.. (2015). A novel “priming-boosting” strategy for immune interventions in cervical cancer. Molecular Immunology. 64(2). 295–305. 11 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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