Search Results for author:
Found 3 papers, 3 papers with code
Semi-Supervised Segmentation of Functional Tissue Units at the Cellular Level
We present a new method for functional tissue unit segmentation at the cellular level, which utilizes the latest deep learning semantic segmentation approaches together with domain adaptation and semi-supervised learning techniques.
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image
Experimentally, DAD-3DNet outperforms or is comparable to the state-of-the-art models in (i) 3D Head Pose Estimation on AFLW2000-3D and BIWI, (ii) 3D Face Shape Reconstruction on NoW and Feng, and (iii) 3D Dense Head Alignment and 3D Landmarks Estimation on DAD-3DHeads dataset.
Ranked #6 on
Head Pose Estimation
on AFLW2000
FEAR: Fast, Efficient, Accurate and Robust Visual Tracker
In addition, we expand the definition of the model efficiency by introducing FEAR benchmark that assesses energy consumption and execution speed.
Ranked #22 on
Visual Object Tracking
on GOT-10k
