Lim, Ser-Nam

What Makes Fake Images Detectable? Understanding Properties that Generalize

Chai, L., Bau, D., Lim, S.-N., & Isola, P. (2020) · Lecture Notes in Computer Science

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 357

AdaViT: Adaptive Vision Transformers for Efficient Image Recognition

Meng, L., Li, H., Chen, B.-C., Lan, S., Wu, Z., Jiang, Y.-G., & Lim, S.-N. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 262

Detecting Deep-Fake Videos from Appearance and Behavior

Agarwal, S., Farid, H., El-Gaaly, T., & Lim, S.-N. (2020) · 2020 IEEE International Workshop on Information Forensics and Security (WIFS)

Digital Media Forensic DetectionFace recognition and analysis

Cited by 242

Joint Audio-Visual Deepfake Detection

Zhou, Y., & Lim, S.-N. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Digital Media Forensic DetectionMisinformation and Its Impacts

Cited by 217

MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding

He, B., Li, H., Jang, Y. K., Jia, M., Cao, X., Shah, A., Shrivastava, A., & Lim, S.-N. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsHuman Pose and Action Recognition

Cited by 94

Learning to Ground Multi-Agent Communication with Autoencoders

Lin, T., Huh, M., Stauffer, C., Lim, S.-N., & Isola, P. (2021) · arXiv

Multi-Agent Systems and NegotiationReinforcement Learning in Robotics

Cited by 26

Multimodal Fusion Refiner Networks

Sankaran, S., Yang, D., & Lim, S.-N. (2021) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 10