activity
20202026
most citedTemporarily-Aware Context Modelling using Generative Adversarial Networks for Speech Activity Detection

9 citations · 11 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation

Sutharsan Mahendran, Darshana Priyasad, Kaushik Roy +4

Cross-modal distillation from Vision Foundation Models (VFMs) to LiDAR backbones has recently emerged as a self-supervised pretraining strategy that reduces reliance on dense point…

cs.LG2025

Transforming volcanic monitoring: A dataset and benchmark for onboard volcano activity detection

Darshana Priyasad, Tharindu Fernando, Maryam Haghighat +2

Natural disasters, such as volcanic eruptions, pose significant challenges to daily life and incur considerable global economic losses. The emergence of next-generation small-satel…

cs.CV2025★ 2 cited

Face Deepfakes -- A Comprehensive Review

Tharindu Fernando, Darshana Priyasad, Sridha Sridharan +2

In recent years, remarkable advancements in deep-fake generation technology have led to unprecedented leaps in its realism and capabilities. Despite these advances, we observe a no…

eess.AS2020

Attention Driven Fusion for Multi-Modal Emotion Recognition

Darshana Priyasad, Tharindu Fernando, Simon Denman +2

Deep learning has emerged as a powerful alternative to hand-crafted methods for emotion recognition on combined acoustic and text modalities. Baseline systems model emotion informa…

cs.LG2020

Memory based fusion for multi-modal deep learning

Darshana Priyasad, Tharindu Fernando, Simon Denman +2

The use of multi-modal data for deep machine learning has shown promise when compared to uni-modal approaches with fusion of multi-modal features resulting in improved performance…

eess.AS2020★ 9 cited

Temporarily-Aware Context Modelling using Generative Adversarial Networks for Speech Activity Detection

Tharindu Fernando, Sridha Sridharan, Mitchell McLaren +3

This paper presents a novel framework for Speech Activity Detection (SAD). Inspired by the recent success of multi-task learning approaches in the speech processing domain, we prop…