17 citations · 18 across the 9 of their papers we have counts for
7 papers · 1 filter
Generative Uncertainty as a Self-supervised Signal for Semantic Similarity Learning
Enrico Pallotta, Sina Raoufi, Lars Doorenbos +2
Evaluating semantic similarity between videos is a fundamental challenge in computer vision, essential for tasks ranging from out-of-distribution (OOD) detection to video retrieval…
Post-Training VLMs for Video Mistake Detection
Federico Spurio, Olga Zatsarynna, Lars Doorenbos +3
Human mistakes are inevitable when following instructions, yet they can lead to severe consequences. As such, there has been an increased interest in developing methods for detecti…
Multi-Person Human Motion Forecasting in Complex Scenes
Serdar Ozsoy, Lars Doorenbos, Juergen Gall
Accurately forecasting the movement of people in complex scenes requires reasoning over the past and present state of the entire environment. In this context, effectively incorpora…
Modality-Aware Out-of-Distribution Detection for Multi-Modal Action Recognition
Lars Doorenbos, Duc Manh Vu, Serdar Ozsoy +1
The incorporation of additional modalities into action recognition models increases their performance across a wide range of settings. However, how this additional information can…
The Unreasonable Effectiveness of VLMs for Zero-shot Procedural Mistake Detection
Serdar Ozsoy, Lars Doorenbos, Federico Spurio +2
Procedural mistake detection is important for quality control and user assistance across many disciplines. Recent work in this field has achieved significant gains by using the rea…
Constrained hybrid modelling to predict microbial dynamics and organic matter turnover in soil systems
Paul Collart, Juergen Gall, Andrea Schnepf +2
Soil microorganisms control organic matter cycling and largely determine how soil systems can cope with and mitigate climate change and environmental threats. Representing microbia…
TRAFA: Anticipating User Actions to Reduce Errors in Procedural Tasks with Predictive Feedback
Sassan Mokhtar, Lars Doorenbos, Fatemeh Jabbari +3
Interactive assistance systems typically provide feedback after an action has been completed, supporting error recovery but not preventing the error itself. We present TRAFA, a rea…