25 citations · 32 across the 11 of their papers we have counts for
12 papers
Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift
Till Beemelmanns, Alexey Nekrasov, Stefan Vilceanu +4
Reliable uncertainty estimation for 3D object detection is critical for deploying safe autonomous systems, yet modern detectors remain poorly calibrated, especially under distribut…
LSVOS 2025 Challenge Report: Recent Advances in Complex Video Object Segmentation
Chang Liu, Henghui Ding, Kaining Ying +46
This report presents an overview of the 7th Large-scale Video Object Segmentation (LSVOS) Challenge held in conjunction with ICCV 2025. Besides the two traditional tracks of LSVOS…
Sa2VA-i: Improving Sa2VA Results with Consistent Training and Inference
Alexey Nekrasov, Ali Athar, Daan de Geus +2
Sa2VA is a recent model for language-guided dense grounding in images and video that achieves state-of-the-art results on multiple segmentation benchmarks and that has become widel…
Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving
Alexey Nekrasov, Malcolm Burdorf, Stewart Worrall +2
To operate safely, autonomous vehicles (AVs) need to detect and handle unexpected objects or anomalies on the road. While significant research exists for anomaly detection and segm…
OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction
Severin Heidrich, Till Beemelmanns, Alexey Nekrasov +2
Autonomous driving has the potential to significantly enhance productivity and provide numerous societal benefits. Ensuring robustness in these safety-critical systems is essential…
Panoptic-CUDAL: Rural Australia Point Cloud Dataset in Rainy Conditions
Tzu-Yun Tseng, Alexey Nekrasov, Malcolm Burdorf +5
Existing autonomous driving datasets are predominantly oriented towards well-structured urban settings and favourable weather conditions, leaving the complexities of rural environm…
MaskTerial: A Foundation Model for Automated 2D Material Flake Detection
Jan-Lucas Uslu, Alexey Nekrasov, Alexander Hermans +4
The detection and classification of exfoliated two-dimensional (2D) material flakes from optical microscope images can be automated using computer vision algorithms. This has the p…
OoDIS: Anomaly Instance Segmentation and Detection Benchmark
Alexey Nekrasov, Rui Zhou, Miriam Ackermann +3
Safe navigation of self-driving cars and robots requires a precise understanding of their environment. Training data for perception systems cannot cover the wide variety of objects…
Mask4Former: Mask Transformer for 4D Panoptic Segmentation
Kadir Yilmaz, Jonas Schult, Alexey Nekrasov +1
Accurately perceiving and tracking instances over time is essential for the decision-making processes of autonomous agents interacting safely in dynamic environments. With this int…
UGainS: Uncertainty Guided Anomaly Instance Segmentation
Alexey Nekrasov, Alexander Hermans, Lars Kuhnert +1
A single unexpected object on the road can cause an accident or may lead to injuries. To prevent this, we need a reliable mechanism for finding anomalous objects on the road. This…
An open-source robust machine learning platform for real-time detection and classification of 2D material flakes
Jan-Lucas Uslu, Taoufiq Ouaj, David Tebbe +8
The most widely used method for obtaining high-quality two-dimensional materials is through mechanical exfoliation of bulk crystals. Manual identification of suitable flakes from t…
Mix3D: Out-of-Context Data Augmentation for 3D Scenes
Alexey Nekrasov, Jonas Schult, Or Litany +2
We present Mix3D, a data augmentation technique for segmenting large-scale 3D scenes. Since scene context helps reasoning about object semantics, current works focus on models with…