1 citations · 1 across the 5 of their papers we have counts for
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…
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…
Probing Fine-Grained Action Understanding and Cross-View Generalization of Foundation Models
Thinesh Thiyakesan Ponbagavathi, Kunyu Peng, Alina Roitberg
Foundation models (FMs) are large neural networks trained on broad datasets, excelling in downstream tasks with minimal fine-tuning. Human activity recognition in video has advance…