16 citations · 16 across the 3 of their papers we have counts for
6 papers
Calibrationless Reconstruction of Uniformly-Undersampled Multi-Channel MR Data with Deep Learning Estimated ESPIRiT Maps
Junhao Zhang, Zheyuan Yi, Yujiao Zhao +8
Purpose: To develop a truly calibrationless reconstruction method that derives ESPIRiT maps from uniformly-undersampled multi-channel MR data by deep learning. Methods: ESPIRiT, on…
A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose
Shih-Yang Su, Frank Yu, Michael Zollhoefer +1
While deep learning reshaped the classical motion capture pipeline with feed-forward networks, generative models are required to recover fine alignment via iterative refinement. Un…
LaNet: Real-time Lane Identification by Learning Road SurfaceCharacteristics from Accelerometer Data
Madhumitha Harishankar, Jun Han, Sai Vineeth Kalluru Srinivas +7
The resolution of GPS measurements, especially in urban areas, is insufficient for identifying a vehicle's lane. In this work, we develop a deep LSTM neural network model LaNet tha…
3D Photography using Context-aware Layered Depth Inpainting
Meng-Li Shih, Shih-Yang Su, Johannes Kopf +1
We propose a method for converting a single RGB-D input image into a 3D photo - a multi-layer representation for novel view synthesis that contains hallucinated color and depth str…
Graph Generation with Variational Recurrent Neural Network
Shih-Yang Su, Hossein Hajimirsadeghi, Greg Mori
Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes. In this paper, we introduce Graph Variational Recurren…
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su +2
Efficient exploration remains a challenging research problem in reinforcement learning, especially when an environment contains large state spaces, deceptive local optima, or spars…