351 citations · 408 across the 11 of their papers we have counts for
12 papers
Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis
Devinder Kumar, Parthipan Siva, Paul Marchwica +1
An ongoing major challenge in computer vision is the task of person re-identification, where the goal is to match individuals across different, non-overlapping camera views. While…
Fairest of Them All: Establishing a Strong Baseline for Cross-Domain Person ReID
Devinder Kumar, Parthipan Siva, Paul Marchwica +1
Person re-identification (ReID) remains a very difficult challenge in computer vision, and critical for large-scale video surveillance scenarios where an individual could appear in…
Beyond Explainability: Leveraging Interpretability for Improved Adversarial Learning
Devinder Kumar, Ibrahim Ben-Daya, Kanav Vats +3
In this study, we propose the leveraging of interpretability for tasks beyond purely the purpose of explainability. In particular, this study puts forward a novel strategy for leve…
SISC: End-to-end Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells
Vignesh Sankar, Devinder Kumar, David A. Clausi +2
Objective: Lung cancer is the leading cause of cancer-related death worldwide. Computer-aided diagnosis (CAD) systems have shown significant promise in recent years for facilitatin…
Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy
Devinder Kumar, Graham W. Taylor, Alexander Wong
Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown considerable promise in recent years as a potential tool for improving clinical decision support in medical onc…
Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction
Devinder Kumar, Graham W Taylor, Alexander Wong
Deep learning has been shown to outperform traditional machine learning algorithms across a wide range of problem domains. However, current deep learning algorithms have been criti…