A paper with the title “Adapting the Segment Anything Model During Usage in Novel Situations” by Robin Sch?n, Julian Lorenz, Katja Ludwig and Rainer Lienhart has been accepted at the workshop for “Efficient Large Vision Models (eLVM)“. The workshop will be held jointly with the CVPR 2024 in Seattle. The paper presents a method for adapting the Segment Anything Model (SAM) during test time without the aid of additional training data. Instead, the method uses information with is generated during usage in order to generate pseudo labels.

[Chair for Machine Learning & Computer Vision]

Mr Harison Kipkulei joined the chair as a research scientist/PostDoc. [Klimaresilienz von Kultur?kosystemen]

?ttl, L.K., Wilken, F., Ju?icová, A., Batista, P.V.G., Fiener, P. (2024) A millennium of arable land use – the long-term impact of tillage and water erosion on landscape-scale carbon dynamics. SOIL 10, 281-305.

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DOI:?https://doi.org/10.5194/soil-10-281-2024

[Water and Soil Resource Research]

The paper "A Review and Efficient Implementation of Scene Graph Generation Metrics" by Julian Lorenz, Robin Sch?n, Katja Ludwig, and Rainer Lienhart is accepted at the Workshop on Scene Graphs and Graph Representation Learning at CVPR 2024.

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The authors review existing scene graph generation metrics and provide precise definitions that were lacking in this field. Additionally, they introduce an efficient and easy to use python package that implements all discussed metrics. To improve comparability of new scene graph generation methods, the authors provide a benchmarking service that enables an easy evaluation of scene graph generation models.

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More information can be found here: https://lorjul.github.io/sgbench/

[Chair for Machine Learning & Computer Vision]

Organische Substanz - Bodenatmung in der Praxis
Die organische Substanz im Boden ist für die Pflanzen überlebensnotwendig. Sie besteht aus einem lebenden Anteil, den Bakterien und einem toten Anteil, dem Humus. [Studienkoordination]

Strategische Vereinbarung unterzeichnet: Um die etablierte vertrauensvolle Zusammenarbeit im KI-Produktionsnetzwerk Augsburg noch st?rker aufzustellen, haben die vier Partner in einem Memorandum of Understanding die Grundlage für die strategische Partnerschaft der n?chsten Jahre gelegt. So soll kurzfristig wirtschaftlicher Impact erzeugt und die langfristige Wettbewerbsf?higkeit der regionalen Wirtschaft gesichert werden.

[中国福彩网_中福在线-千万大奖等你 of Augsburg]