Tianfu Wu is an associate professor in the Department of Electrical and Computer Engineering at NC State University. He is currently the PI of the laboratory of interpretable Visual Modeling, Computing and Learning (iVMCL). He received his Ph.D. in statistics from UCLA under the supervision by Prof. Song-Chun Zhu. His research focuses on interpretable Visual Modeling, Computing and Learning, often motivated by the tasks of pursuing a unified framework for AI to ALTER (Ask, Learn, Test, Explain and Refine) in a trustworthy, robust and responsive way for AIGCGT (AI Generated Content and Ground-Truth).
University of California, Los Angeles
- Level-S2fM: Structure from Motion on Neural Level Set of Implicit Surfaces (2023)
- PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers (2023)
- Thermal Estimation for 3D-ICs through Generative Networks (2023)
- Volumetric Wireframe Parsing from Neural Attraction Fields (2023)
- HoW-3D: Holistic 3DWireframe Perception from a Single Image (2022)
- Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning (2022)
- Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis (2022)
- Learning Local-Global Contextual Adaptation for Multi-Person Pose Estimation (2022)
- Modeling of Adaptive Receiver Performance Using Generative Adversarial Networks (2022)
- Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working Dogs (2022)
Posted on September 28, 2023 | Filed Under: AI/ML and Research
Photos are two-dimensional (2D), but autonomous vehicles and other technologies have to navigate the three-dimensional (3D) world. Researchers have developed a new method to help artificial intelligence (AI) extract 3D information from 2D i …
Posted on June 2, 2023 | Filed Under: AI/ML and Research
The work also improves the vision transformer AI’s ability to identify, classify and segment objects in images.
Posted on January 13, 2023 | Filed Under: News
Priyank Kashyup and Yuejiang Wen each won the Best Poster Award at CAEML’s Fall 2022 Semiannual Meeting.