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20112024
most citedUnsupervised Anomaly Detection for X-Ray Images

10 citations · 41 across the 20 of their papers we have counts for

collaborators

20 papers

cs.AI2024★ 5 cited

A Time-Inhomogeneous Markov Model for Resource Availability under Sparse Observations

Lukas Rottkamp, Matthias Schubert

Accurate spatio-temporal information about the current situation is crucial for smart city applications such as modern routing algorithms. Often, this information describes the sta…

cs.CV2023★ 2 cited

GRAtt-VIS: Gated Residual Attention for Auto Rectifying Video Instance Segmentation

Tanveer Hannan, Rajat Koner, Maximilian Bernhard +5

Recent trends in Video Instance Segmentation (VIS) have seen a growing reliance on online methods to model complex and lengthy video sequences. However, the degradation of represen…

cs.CV2023

MapFormer: Boosting Change Detection by Using Pre-change Information

Maximilian Bernhard, Niklas Strauß, Matthias Schubert

Change detection in remote sensing imagery is essential for a variety of applications such as urban planning, disaster management, and climate research. However, existing methods f…

cs.CV2022

Robust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations (Full Version)

Maximilian Bernhard, Matthias Schubert

Recently, the availability of remote sensing imagery from aerial vehicles and satellites constantly improved. For an automated interpretation of such data, deep-learning-based obje…

cs.CL2022★ 1 cited

Federated Continual Learning for Text Classification via Selective Inter-client Transfer

Yatin Chaudhary, Pranav Rai, Matthias Schubert +2

In this work, we combine the two paradigms: Federated Learning (FL) and Continual Learning (CL) for text classification task in cloud-edge continuum. The objective of Federated Con…

cs.CV2022★ 3 cited

InstanceFormer: An Online Video Instance Segmentation Framework

Rajat Koner, Tanveer Hannan, Suprosanna Shit +4

Recent transformer-based offline video instance segmentation (VIS) approaches achieve encouraging results and significantly outperform online approaches. However, their reliance on…