Siyang Song

1.2k total citations
64 papers, 597 citations indexed

About

Siyang Song is a scholar working on Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology and Cognitive Neuroscience. According to data from OpenAlex, Siyang Song has authored 64 papers receiving a total of 597 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Computer Vision and Pattern Recognition, 23 papers in Experimental and Cognitive Psychology and 12 papers in Cognitive Neuroscience. Recurrent topics in Siyang Song's work include Emotion and Mood Recognition (23 papers), Face recognition and analysis (21 papers) and Generative Adversarial Networks and Image Synthesis (11 papers). Siyang Song is often cited by papers focused on Emotion and Mood Recognition (23 papers), Face recognition and analysis (21 papers) and Generative Adversarial Networks and Image Synthesis (11 papers). Siyang Song collaborates with scholars based in United Kingdom, China and United States. Siyang Song's co-authors include Linlin Shen, Michel Valstar, Shashank Jaiswal, Hatice Güneş, Weicheng Xie, Enrique Sánchez, Xiaofeng Liu, Georgios Tzimiropoulos, Rongrong Ni and Angelo Cangelosi and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Image Processing and Pattern Recognition.

In The Last Decade

Siyang Song

53 papers receiving 583 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Siyang Song United Kingdom 13 303 261 123 108 88 64 597
Evangelos Sarıyanidi United States 9 426 1.4× 482 1.8× 78 0.6× 134 1.2× 90 1.0× 29 707
Josep M. Gonfaus Spain 7 142 0.5× 311 1.2× 65 0.5× 50 0.5× 97 1.1× 12 525
Ognjen Rudovic United States 17 571 1.9× 726 2.8× 107 0.9× 196 1.8× 217 2.5× 45 1.1k
Timur Almaev United Kingdom 7 559 1.8× 408 1.6× 140 1.1× 117 1.1× 166 1.9× 7 767
Ming-Hsiang Su Taiwan 12 238 0.8× 81 0.3× 73 0.6× 63 0.6× 283 3.2× 48 567
Yuanyuan Shang China 19 411 1.4× 677 2.6× 174 1.4× 133 1.2× 156 1.8× 37 1.1k
Barnabás Takács Hungary 13 82 0.3× 281 1.1× 48 0.4× 69 0.6× 28 0.3× 52 580
Tanaya Guha United Kingdom 15 163 0.5× 509 2.0× 64 0.5× 150 1.4× 241 2.7× 53 865
Radu L. Vieriu Italy 4 279 0.9× 79 0.3× 56 0.5× 211 2.0× 78 0.9× 5 430
Florian Lingenfelser Germany 12 240 0.8× 128 0.5× 222 1.8× 89 0.8× 266 3.0× 36 599

Countries citing papers authored by Siyang Song

Since Specialization
Citations

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

Fields of papers citing papers by Siyang Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Siyang Song

This figure shows the co-authorship network connecting the top 25 collaborators of Siyang Song. A scholar is included among the top collaborators of Siyang Song 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 Siyang Song. Siyang Song 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.
Xie, Weicheng, et al.. (2025). Frequency Restoration and Modality Enforcement towards Resisting-corruption Multimodal Sentiment Analysis. ACM Transactions on Multimedia Computing Communications and Applications. 21(12). 1–24.
2.
Li, Fangjun, et al.. (2025). Learning from Human Conversations: A Seq2Seq based Multi-modal Robot Facial Expression Reaction Framework in HRI. Research Explorer (The University of Manchester). 7261–7268.
3.
Yang, Fan, et al.. (2025). SymGraphAU: Prior knowledge based symbolic graph for action unit recognition. Pattern Recognition. 165. 111640–111640.
4.
Ma, Xiaowen, et al.. (2025). LOGCAN++: Adaptive Local-Global Class-Aware Network for Semantic Segmentation of Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–16. 8 indexed citations
5.
6.
Ma, Xiaowen, et al.. (2025). Deep spatial–spectral fusion transformer for remote sensing pansharpening. Information Fusion. 118. 102980–102980. 2 indexed citations
7.
Song, Siyang, Micol Spitale, Hengde Zhu, et al.. (2025). REACT 2025: the Third Multiple Appropriate Facial Reaction Generation Challenge. VBN Forskningsportal (Aalborg Universitet). 13979–13984.
8.
Song, Siyang, et al.. (2024). Loss Relaxation Strategy for Noisy Facial Video-based Automatic Depression Recognition. Apollo (University of Cambridge). 5(2). 1–24. 2 indexed citations
9.
Song, Siyang, Micol Spitale, Cheng Luo, et al.. (2024). REACT 2024: the Second Multiple Appropriate Facial Reaction Generation Challenge. 1–5. 6 indexed citations
10.
Liu, Xiaofeng, Rongrong Ni, Biao Yang, Siyang Song, & Angelo Cangelosi. (2024). Unlocking Human-Like Facial Expressions in Humanoid Robots: A Novel Approach for Action Unit Driven Facial Expression Disentangled Synthesis. IEEE Transactions on Robotics. 40. 3850–3865. 3 indexed citations
11.
Li, Xiangdong, et al.. (2024). Supervised Detail-Guided Multiscale State-Space Model for Pan-Sharpening. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–16. 2 indexed citations
12.
Fu, Changzeng, Siyang Song, Mingyue Niu, et al.. (2024). Facial action units guided graph representation learning for multimodal depression detection. Neurocomputing. 619. 129106–129106. 5 indexed citations
13.
Song, Siyang, et al.. (2024). Multi-modal Human Behaviour Graph Representation Learning for Automatic Depression Assessment. Apollo (University of Cambridge). 1–10. 2 indexed citations
14.
Xie, Weicheng, Z. Y. Peng, Linlin Shen, et al.. (2024). Cross-Layer Contrastive Learning of Latent Semantics for Facial Expression Recognition. IEEE Transactions on Image Processing. 33. 2514–2529. 9 indexed citations
15.
Luo, Cheng, et al.. (2024). Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping. Proceedings of the AAAI Conference on Artificial Intelligence. 38(4). 3459–3467. 3 indexed citations
16.
Song, Siyang, Cheng Luo, Andrew Mitchell, et al.. (2023). Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation Learning. Ghent University Academic Bibliography (Ghent University). 331–335. 4 indexed citations
17.
Song, Siyang, et al.. (2022). Learning Person-Specific Cognition From Facial Reactions for Automatic Personality Recognition. IEEE Transactions on Affective Computing. 14(4). 3048–3065. 27 indexed citations
18.
Song, Siyang, Shashank Jaiswal, Enrique Sánchez, et al.. (2021). Self-Supervised Learning of Person-Specific Facial Dynamics for Automatic Personality Recognition. IEEE Transactions on Affective Computing. 14(1). 178–195. 29 indexed citations
19.
Song, Siyang, Shashank Jaiswal, Linlin Shen, & Michel Valstar. (2020). Spectral Representation of Behaviour Primitives for Depression Analysis. IEEE Transactions on Affective Computing. 13(2). 829–844. 97 indexed citations
20.
Song, Siyang, Linlin Shen, & Michel Valstar. (2018). Human Behaviour-Based Automatic Depression Analysis Using Hand-Crafted Statistics and Deep Learned Spectral Features. Repository@Nottingham (University of Nottingham). 158–165. 78 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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