Search Results for author:
Found 7 papers, 2 papers with code
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.
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.
HashReID: Dynamic Network with Binary Codes for Efficient Person Re-identification
Using our approach, more than 70% of the samples with compact hash codes exit early on the Market1501 dataset, saving 80% of the networks computational cost and improving over other hash-based methods by 60%.
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.
Hyperspectral Image Classification with Attention Aided CNNs
Specifically, a spectral attention sub-network and a spatial attention sub-network are proposed for spectral and spatial classification, respectively.
Elastic Neural Networks for Classification
In this work we propose a framework for improving the performance of any deep neural network that may suffer from vanishing gradients.
Elastic Neural Networks: A Scalable Framework for Embedded Computer Vision
We propose a new framework for image classification with deep neural networks.
