$CrowdDiff$: Multi-hypothesis Crowd Density Estimation using Diffusion Models

1 code implementation • 22 Mar 2023 • Yasiru Ranasinghe, Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M. Patel

Furthermore, as the intermediate time steps of the diffusion process are noisy, we incorporate a regression branch for direct crowd estimation only during training to improve the feature learning.

Contour Detection Crowd Counting +1

GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness

no code implementations • 16 Apr 2022 • Yasiru Ranasinghe, Kavinga Weerasooriya, Roshan Godaliyadda, Vijitha Herath, Parakrama Ekanayake, Dhananjaya Jayasundara, Lakshitha Ramanayake, Neranjan Senarath, Dulantha Wickramasinghe

In the Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness (GAUSS), we proposed using one-hot encoded abundances as the pseudo-ground truth to guide the UN; computed using the k-means algorithm to exclude the use of prior HU methods.

Hyperspectral Unmixing

Convolutional Autoencoder for Blind Hyperspectral Image Unmixing

no code implementations • 18 Nov 2020 • Yasiru Ranasinghe, Sanjaya Herath, Kavinga Weerasooriya, Mevan Ekanayake, Roshan Godaliyadda, Parakrama Ekanayake, Vijitha Herath

In the remote sensing context spectral unmixing is a technique to decompose a mixed pixel into two fundamental representatives: endmembers and abundances.