Search Results for author:
Found 6 papers, 0 papers with code
Diversifying Human Pose in Synthetic Data for Aerial-view Human Detection
We present a framework for diversifying human poses in a synthetic dataset for aerial-view human detection.
Exploring the Impact of Synthetic Data for Aerial-view Human Detection
Therefore, synthetic data can be a good resource to expand data, but the domain gap with real-world data is the biggest obstacle to its use in training.
MEMO: Dataset and Methods for Robust Multimodal Retinal Image Registration with Large or Small Vessel Density Differences
To fill this gap, we establish MEMO, the first public multimodal EMA and OCTA retinal image dataset.
Progressive Transformation Learning for Leveraging Virtual Images in Training
To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instances with various poses captured from widely varying viewing angles.
Archangel: A Hybrid UAV-based Human Detection Benchmark with Position and Pose Metadata
Learning to detect objects, such as humans, in imagery captured by an unmanned aerial vehicle (UAV) usually suffers from tremendous variations caused by the UAV's position towards the objects.
What Synthesis is Missing: Depth Adaptation Integrated with Weak Supervision for Indoor Scene Parsing
Our proposed two-stage integration more than halves the gap towards fully supervised methods when compared to previous state-of-the-art in transfer learning.
