5 papers
Frame2Freq: Spectral Adapters for Fine-Grained Video Understanding
Thinesh Thiyakesan Ponbagavathi, Constantin Seibold, Alina Roitberg
Adapting image-pretrained backbones to video typically relies on time-domain adapters tuned to a single temporal scale. Our experiments show that these modules pick up static image…
Deep Learning for Metabolic Rate Estimation from Biosignals: A Comparative Study of Architectures and Signal Selection
Sarvenaz Babakhani, David Remy, Alina Roitberg
Energy expenditure estimation aims to infer human metabolic rate from physiological signals such as heart rate, respiration, or accelerometer data, and has been studied primarily w…
T-MASK: Temporal Masking for Probing Foundation Models across Camera Views in Driver Monitoring
Thinesh Thiyakesan Ponbagavathi, Kunyu Peng, Alina Roitberg
Changes of camera perspective are a common obstacle in driver monitoring. While deep learning and pretrained foundation models show strong potential for improved generalization via…
Structured Relational Reasoning for Group Activity Assessment
Thinesh Thiyakesan Ponbagavathi, Chengzheng Yang, Alina Roitberg
Group Activity Detection (GAD) involves recognizing social groups and their collective behaviors in videos. Vision Foundation Models (VFMs), like DINOv2, offer excellent features b…
Order Matters: On Parameter-Efficient Image-to-Video Probing for Recognizing Nearly Symmetric Actions
Thinesh Thiyakesan Ponbagavathi, Alina Roitberg
Fine-grained understanding of human actions is essential for safe and intuitive human--robot interaction. We study the challenge of recognizing nearly symmetric actions, such as pi…