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Found 5 papers, 3 papers with code
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
Robust Audio-Based Vehicle Counting in Low-to-Moderate Traffic Flow
The method is trained and tested on a traffic-monitoring dataset comprising $422$ short, $20$-second one-channel sound files with a total of $ 1421 $ vehicles passing by the microphone.
Neural Network-based Acoustic Vehicle Counting
This distance is predicted from audio using a two-stage (coarse-fine) regression, with both stages realised via neural networks (NNs).
ALFA: Agglomerative Late Fusion Algorithm for Object Detection
We propose ALFA - a novel late fusion algorithm for object detection.
