Search results for: Yasaswi Palagummi

Authors: Yasaswi Palagummi, Sareh Rowlands

Abstract:

Generalised Zero-Shot Learning, often known as GZSL, is an advanced variant of zero-shot learning in which the samples in the unseen category may be either seen or unseen. GZSL methods typically have a bias towards the seen classes because they learn a model to perform recognition for both the seen and unseen classes using data samples from the seen classes. This frequently leads to the misclassification of data from the unseen classes into the seen classes, making the task of GZSL more challenging. In this work of ours, to solve the GZSL problem, we propose an approach leveraging the Shifted Window based Self-Attention in the Swin Transformer (Swin-GZSL) to work in the inductive GSZL problem setting. We run experiments on three popular benchmark datasets: CUB, SUN, and AWA2, which are specifically used for ZSL and its other variants. The results show that our model based on Swin Transformer has achieved state-of-the-art harmonic mean for two datasets -AWA2 and SUN and near-state-of-the-art for the other dataset - CUB. More importantly, this technique has a linear computational complexity, which reduces training time significantly. We have also observed less bias than most of the existing GZSL models.

Keywords: generalised, zero-shot learning, inductive learning, shifted-window attention, Swin transformer, vision transformer

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Authors: Yasaswi Vengalasetti, Jacob Eisenach, Jay Bhattacharya

Abstract:

Seroprevalence studies can provide an estimation of the Infection Fatality Rate (IFR), the probability of death given infection. Measuring the seroprevalence and reported deaths of an area within a given time frame an IFR can be estimated. With this IFR calculation, we can then observe COVID-19 death figures in different countries around the world and estimate the number of cases since the onset of the pandemic. There is a large range for estimated COVID-19 infections across different countries. This ranged from 0.659 million infections in Hong Kong to 277 million infections in India. The largest estimated share of the population infected is 63% in Peru and the lowest is 3% in Norway. For younger populations, COVID-19 is most fatal in South America; for older populations, it is most fatal in North America. The Asian regions stand out with significantly lower IFRs in older populations: at 80 years old, COVID-19 is about three times as fatal than in South Asia and about twelve times as fatal than in East Asia. The weighted average for the share of the population infected, the sum of infections divided by the sum of populations across all countries, is 23%.

Keywords: epidemiology, seroprevalence, covid-19, infection fatality rate

Procedia PDF Downloads 92