6 citations · 6 across the 9 of their papers we have counts for
9 papers
Spotted: Location-informed Reidentification of Hyenas and Leopards in Camera Trap Surveys
Halil Sina Kelebek, Julia Hindel, Kobus Hoffman +9
Animal re-identification (ReID) in camera-trap surveys remains challenging due to low image quality, strong variation in illumination and viewpoint, and highly imbalanced numbers o…
Self-Supervised Online Robot-Agnostic Traversability Estimation for Open-World Environments
Julia Hindel, Simon Bultmann, Houman Masnavi +2
Self-supervised online traversability estimation enables robots to continuously learn from unlabeled open-world experiences and adapt their navigation behavior toward safe and effi…
Assessing Multimodal Chronic Wound Embeddings with Expert Triplet Agreement
Fabian Kabus, Julia Hindel, Jelena Bratulić +6
Recessive dystrophic epidermolysis bullosa (RDEB) is a rare genetic skin disorder for which clinicians greatly benefit from finding similar cases using images and clinical text. Ho…
Embedding interpretable -regression into neural networks for uncovering temporal structure in cell imaging
Fabian Kabus, Maren Hackenberg, Julia Hindel +6
While artificial neural networks excel in unsupervised learning of non-sparse structure, classical statistical regression techniques offer better interpretability, in particular wh…
Dynamic Robot-Assisted Surgery with Hierarchical Class-Incremental Semantic Segmentation
Julia Hindel, Ema Mekic, Enamundram Naga Karthik +4
Robot-assisted surgeries rely on accurate and real-time scene understanding to safely guide surgical instruments. However, segmentation models trained on static datasets face key l…
Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning
Rohit Mohan, Julia Hindel, Florian Drews +3
Autonomous vehicles that navigate in open-world environments may encounter previously unseen object classes. However, most existing LiDAR panoptic segmentation models rely on close…
Label-Efficient LiDAR Semantic Segmentation with 2D-3D Vision Transformer Adapters
Julia Hindel, Rohit Mohan, Jelena Bratulic +3
LiDAR semantic segmentation models are typically trained from random initialization as universal pre-training is hindered by the lack of large, diverse datasets. Moreover, most poi…
Taxonomy-Aware Continual Semantic Segmentation in Hyperbolic Spaces for Open-World Perception
Julia Hindel, Daniele Cattaneo, Abhinav Valada
Semantic segmentation models are typically trained on a fixed set of classes, limiting their applicability in open-world scenarios. Class-incremental semantic segmentation aims to…
INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fields
Julia Hindel, Nikhil Gosala, Kevin Bregler +1
Perception datasets for agriculture are limited both in quantity and diversity which hinders effective training of supervised learning approaches. Self-supervised learning techniqu…