The paper "A Fair Ranking and New Model for Panoptic Scene Graph Generation" by Julian Lorenz, Alexander Pest, Daniel Kienzle, Katja Ludwig, and Rainer Lienhart has been accepted for ECCV 2024.

The authors discuss significant flaws in commonly used evaluation protocols for Panoptic Scene Graph Generation. They present a solution to this problem and evaluate existing publications based on the new findings.
Finally, a new state-of-the-art architecture for Panoptic Scene Graph Generation is presented.

More information can be found here: https://lorjul.github.io/fair-psgg/

A paper with the title “Adapting 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.