4 citations · 9 across the 5 of their papers we have counts for
7 papers · 1 filter
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…
Towards Human-Interpretable Prototypes for Visual Assessment of Image Classification Models
Poulami Sinhamahapatra, Lena Heidemann, Maureen Monnet +1
Explaining black-box Artificial Intelligence (AI) models is a cornerstone for trustworthy AI and a prerequisite for its use in safety critical applications such that AI models can…
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space
Poulami Sinhamahapatra, Rajat Koner, Karsten Roscher +1
It is essential for safety-critical applications of deep neural networks to determine when new inputs are significantly different from the training distribution. In this paper, we…
Scenes and Surroundings: Scene Graph Generation using Relation Transformer
Rajat Koner, Poulami Sinhamahapatra, Volker Tresp
Identifying objects in an image and their mutual relationships as a scene graph leads to a deep understanding of image content. Despite the recent advancement in deep learning, the…
OODformer: Out-Of-Distribution Detection Transformer
Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher +2
A serious problem in image classification is that a trained model might perform well for input data that originates from the same distribution as the data available for model train…