collaborators

11 papers

cs.CV2026

From Vision to Text: A Compact Multimodal Approach for Robust, Cross-Domain Presentation Attack Detection on ID Cards

Qingwen Zeng, Juan E. Tapia, Sneha Das +1

Cross-domain shifts challenge Presentation Attack Detection (PAD) on ID Cards, given the restricted data available due to privacy concerns. This work proposes a compact multimodal…

eess.AS2026

Modelling Emotions is an Elusive Pursuit in Affective Computing

Anders Rolighed Larsen, Sneha Das, Nicole Nadine Lønfeldt +2

Affective computing - combining sensor technology, machine learning, and psychology - have been studied for over three decades and is employed in AI-powered technologies to enhance…

cs.LG2026

Beyond Word Error Rate: Auditing the Diversity Tax in Speech Recognition through Dataset Cartography

Ting-Hui Cheng, Line H. Clemmensen, Sneha Das

Automatic speech recognition (ASR) systems are predominantly evaluated using the Word Error Rate (WER). However, raw token-level metrics fail to capture semantic fidelity and routi…

cs.CL2026

Measuring What VLMs Don't Say: Validation Metrics Hide Clinical Terminology Erasure in Radiology Report Generation

Aditya Parikh, Aasa Feragen, Sneha Das +1

Reliable deployment of Vision-Language Models (VLMs) in radiology requires validation metrics that go beyond surface-level text similarity to ensure clinical fidelity and demograph…

cs.LG2026

Intra-Fairness Dynamics: The Bias Spillover Effect in Targeted LLM Alignment

Eva Paraschou, Line Harder Clemmensen, Sneha Das

Conventional large language model (LLM) fairness alignment largely focuses on mitigating bias along single sensitive attributes, overlooking fairness as an inherently multidimensio…

eess.IV2025

Investigating Label Bias and Representational Sources of Age-Related Disparities in Medical Segmentation

Aditya Parikh, Sneha Das, Aasa Feragen

Algorithmic bias in medical imaging can perpetuate health disparities, yet its causes remain poorly understood in segmentation tasks. While fairness has been extensively studied in…