Search Results for author:
Found 11 papers, 1 papers with code
Structured Radial Basis Function Network: Modelling Diversity for Multiple Hypotheses Prediction
A closed-form solution with least-squares is presented, which to the authors knowledge, is the fastest solution in the literature for multiple hypotheses and structured predictions.
Deep Unsupervised Learning for 3D ALS Point Cloud Change Detection
To circumnavigate this dependence, we propose an unsupervised 3D point cloud change detection method mainly based on self-supervised learning using deep clustering and contrastive learning.
Deep dual stream residual network with contextual attention for pansharpening of remote sensing images
There are a number of traditional pansharpening approaches but producing an image exhibiting high spectral and spatial fidelity is still an open problem.
Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends
Person re-identification (Re-ID) is one of the primary components of an automated visual surveillance system.
Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network
The IoU and BFScore of the proposed model were 0. 9311, 0. 9138, and 0. 9032, 0. 8978 on healthy and anaemic datasets, respectively.
Orientation Aware Weapons Detection In Visual Data : A Benchmark Dataset
For training our model for weapon detection a new dataset comprising of total 6400 weapons images is gathered from the web and then manually annotated with position oriented bounding boxes.
Segmentation of VHR EO Images using Unsupervised Learning
The proposed method exploits this property to sample smaller patches from the larger scene and uses deep clustering and contrastive learning to refine the weights of a lightweight deep model composed of a series of the convolution layers along with an embedded channel attention.
A Survey of Uncertainty in Deep Neural Networks
Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications.
Exploring Deep 3D Spatial Encodings for Large-Scale 3D Scene Understanding
In this letter, we have proposed an alternative approach to overcome the limitations of CNN based approaches by encoding the spatial features of raw 3D point clouds into undirected symmetrical graph models.
Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Image
We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model.
Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks
This paper addresses the highly challenging problem of automatically detecting man-made structures especially buildings in very high resolution (VHR) synthetic aperture radar (SAR) images.
