106 citations · 312 across the 67 of their papers we have counts for
11 papers · 1 filter
Unexplored flaws in multiple-choice VQA make benchmarking unreliable
Fabio Rosenthal, Sebastian Schmidt, Thorsten Graf +3
Previous works identify sensitivity to option order as a key issue in multiple-choice VQA (MC-VQA) evaluation and propose protocols to mitigate this effect. We show that such mitig…
A Machine Learning Perspective on Automated Driving Corner Cases
Sebastian Schmidt, Julius Körner, Stephan Günnemann
For high-stakes applications, like autonomous driving, a safe operation is necessary to prevent harm, accidents, and failures. Traditionally, difficult scenarios have been categori…
Joint Out-of-Distribution Filtering and Data Discovery Active Learning
Sebastian Schmidt, Leonard Schenk, Leo Schwinn +1
As the data demand for deep learning models increases, active learning (AL) becomes essential to strategically select samples for labeling, which maximizes data efficiency and redu…
A Unified Approach Towards Active Learning and Out-of-Distribution Detection
Sebastian Schmidt, Leonard Schenk, Leo Schwinn +1
When applying deep learning models in open-world scenarios, active learning (AL) strategies are crucial for identifying label candidates from a nearly infinite amount of unlabeled…
Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of Out-of-Distribution Objects Using Prototypes
Poulami Sinhamahapatra, Franziska Schwaiger, Shirsha Bose +3
Detecting and localising unknown or out-of-distribution (OOD) objects in any scene can be a challenging task in vision, particularly in safety-critical cases involving autonomous s…
Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes
Poulami Sinhamahapatra, Suprosanna Shit, Anjany Sekuboyina +7
Vertebral fracture grading classifies the severity of vertebral fractures, which is a challenging task in medical imaging and has recently attracted Deep Learning (DL) models. Only…