activity
20172022
most citedA Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection

21 citations · 107 across the 27 of their papers we have counts for

collaborators

61 papers

cs.CV2021

Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalization

Mohammad Mahfujur Rahman, Clinton Fookes, Sridha Sridharan

Domain generalization approaches aim to learn a domain invariant prediction model for unknown target domains from multiple training source domains with different distributions. Sig…

eess.IV2021

Multi-Slice Net: A novel light weight framework for COVID-19 Diagnosis

Harshala Gammulle, Tharindu Fernando, Sridha Sridharan +2

This paper presents a novel lightweight COVID-19 diagnosis framework using CT scans. Our system utilises a novel two-stage approach to generate robust and efficient diagnoses acros…

cs.SD2021

Robust and Interpretable Temporal Convolution Network for Event Detection in Lung Sound Recordings

Tharindu Fernando, Sridha Sridharan, Simon Denman +2

This paper proposes a novel framework for lung sound event detection, segmenting continuous lung sound recordings into discrete events and performing recognition on each event. Exp…

cs.CV2021

Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification

Amena Khatun, Simon Denman, Sridha Sridharan +1

In a real world environment, person re-identification (Re-ID) is a challenging task due to variations in lighting conditions, viewing angles, pose and occlusions. Despite recent pe…

cs.CV2021

Pose-driven Attention-guided Image Generation for Person Re-Identification

Amena Khatun, Simon Denman, Sridha Sridharan +1

Person re-identification (re-ID) concerns the matching of subject images across different camera views in a multi camera surveillance system. One of the major challenges in person…

cs.CV2021

Preserving Semantic Consistency in Unsupervised Domain Adaptation Using Generative Adversarial Networks

Mohammad Mahfujur Rahman, Clinton Fookes, Sridha Sridharan

Unsupervised domain adaptation seeks to mitigate the distribution discrepancy between source and target domains, given labeled samples of the source domain and unlabeled samples of…