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Found 8 papers, 2 papers with code
Iso-Diffusion: Improving Diffusion Probabilistic Models Using the Isotropy of the Additive Gaussian Noise
Thus, we were motivated to utilize the isotropy of the additive noise as a constraint on the objective function to enhance the fidelity of DDPMs.
GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness
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.
Holistic Interpretation of Public Scenes Using Computer Vision and Temporal Graphs to Identify Social Distancing Violations
The system strives to holistically capture and interpret the information content of CCTV footage spanning multiple frames to recognize instances of various violations of social distancing protocols, across time and space, as well as identification of group behaviors.
Hands Off: A Handshake Interaction Detection and Localization Model for COVID-19 Threat Control
The proposed model is the first dyadic interaction localizer in a multi-person setting, which enables it to be used in public spaces to identify handshake interactions and thereby identify and mitigate COVID-19 transmission.
A generalized forecasting solution to enable future insights of COVID-19 at sub-national level resolutions
In this study, a forecasting solution is proposed, to predict daily new cases of COVID-19 in regions small enough where containment measures could be locally implemented, by targeting three main shortcomings that exist in literature; the unreliability of existing data caused by inconsistent testing patterns in smaller regions, weak deploy-ability of forecasting models towards predicting cases in previously unseen regions, and model training biases caused by the imbalanced nature of data in COVID-19 epi-curves.
An Optical physics inspired CNN approach for intrinsic image decomposition
There is a lack of unsupervised learning approaches for decomposing an image into reflectance and shading using a single image.
Convolutional Autoencoder for Blind Hyperspectral Image Unmixing
In the remote sensing context spectral unmixing is a technique to decompose a mixed pixel into two fundamental representatives: endmembers and abundances.
A Retinex based GAN Pipeline to Utilize Paired and Unpaired Datasets for Enhancing Low Light Images
Low light image enhancement is an important challenge for the development of robust computer vision algorithms.
