Song Park
Song Park

Research Scientist

NAVER AI Lab.

About Me

I am a Research Scientist at NAVER AI Lab, interested in how deep neural networks perceive and process diverse visual concepts to enhance structured visual representations for real-world applications. I received my Ph.D. from Yonsei University in 2022 under the supervision of Hyunjung Shim. Before joining NAVER AI Lab, I worked as a research intern at NAVER CLOVA in 2020.

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Interests
  • Text-to-Image Generative Models
  • Visual Representation Learning
Education
  • Ph.D. and M.S. in Integrated Technology

    Yonsei University

  • B.S. in Integrated Technology

    Yonsei University

Research Interests

I am interested in understanding how deep neural networks (DNNs) perceive, represent, and process diverse visual concepts—such as mood, emotion, style, and semantics—and their impact on decision-making. My research aims to uncover the underlying mechanisms behind these representations to develop more structured and expressive visual features.

By enhancing the interpretability and robustness of learned representations, I seek to improve performance in real-world downstream tasks, including scene understanding, affective computing, and content generation.

Recent Publications
(2025). DNNs May Determine Major Properties of Their Outputs Early, with Timing Possibly Driven by Bias. arXiv preprint arXiv:2502.08167.
(2024). Probabilistic Language-Image Pre-Training. arXiv preprint arXiv:2410.18857.
(2024). Rotary position embedding for vision transformer. European Conference on Computer Vision.
(2024). SeiT++: Masked Token Modeling Improves Storage-efficient Training. European Conference on Computer Vision.
(2024). Similarity of neural architectures using adversarial attack transferability. European Conference on Computer Vision.