May 24, 2024

Paper accepted at International Conference on Multimedia Information Processing and Retrieval (MIPR) 2024

The paper titled "Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic Segmentation" by Daniel Kienzle, Marco Kantonis, Robin Schön, and Rainer Lienhart has been accepted at the IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) 2024. The paper describes a new method to enhance the efficiency of transformer models. This enables the application of computationally intensive transformer models to high-resolution images.

Further information about this paper can be found at

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April 18, 2024

Paper accepted at the eLVM@CVPR 2024 workshop

A paper with the titleAdapting the Segment Anything Model During Usage in Novel Situations” by Robin Schön, 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.

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April 17, 2024

Paper for SG2RL@CVPR 2024 accepted

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


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.


More information can be found here:

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Prof. Dr. Rainer Lienhart

Lehrstuhl für Maschinelles Lernen und Maschinelles Sehen

Institut für Informatik

Universität Augsburg

Universitätsstr. 6a

D -       89159 Augsburg




+49 (821) 598-5703



rainer.lienhart @informatik.uni-



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