Search Results for author:
Found 7 papers, 1 papers with code
Trustworthiness of Pretrained Transformers for Lung Cancer Segmentation
We assessed the trustworthiness of two self-supervision pretrained transformer models, Swin UNETR and SMIT, for fine-tuned lung (LC) tumor segmentation using 670 CT and MRI scans.
Semantic Segmentation with Active Semi-Supervised Representation Learning
We achieve more than 95% of the network's performance on CamVid and CityScapes datasets, utilizing only 12. 1% and 15. 1% of the labeled data, respectively.
Semantic Segmentation with Active Semi-Supervised Learning
Instead, our active learning approach aims to minimize the number of annotations per-image.
AeroRIT: A New Scene for Hyperspectral Image Analysis
We investigate applying convolutional neural network (CNN) architecture to facilitate aerial hyperspectral scene understanding and present a new hyperspectral dataset-AeroRIT-that is large enough for CNN training.
Aerial Spectral Super-Resolution using Conditional Adversarial Networks
In contrast to the spectra of ground based images, aerial spectral images have low spatial resolution and suffer from higher noise interference.
Tracking in Aerial Hyperspectral Videos using Deep Kernelized Correlation Filters
Hyperspectral imaging holds enormous potential to improve the state-of-the-art in aerial vehicle tracking with low spatial and temporal resolutions.
Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps
Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks.
