Search Results for author: Julian Lorenz

Found 9 papers, 5 papers with code

A Fair Ranking and New Model for Panoptic Scene Graph Generation

no code implementations12 Jul 2024 Julian Lorenz, Alexander Pest, Daniel Kienzle, Katja Ludwig, Rainer Lienhart

The observed scores for existing methods increase by up to 7. 4 mR@50 for all two-stage methods, while dropping by up to 19. 3 mR@50 for all one-stage methods, highlighting the importance of a correct evaluation.

A Review and Efficient Implementation of Scene Graph Generation Metrics

no code implementations15 Apr 2024 Julian Lorenz, Robin Schön, Katja Ludwig, Rainer Lienhart

Scene graph generation has emerged as a prominent research field in computer vision, witnessing significant advancements in the recent years.

Benchmarking Graph Generation +1

Adapting the Segment Anything Model During Usage in Novel Situations

no code implementations12 Apr 2024 Robin Schön, Julian Lorenz, Katja Ludwig, Rainer Lienhart

The interactive segmentation task consists in the creation of object segmentation masks based on user interactions.

Interactive Segmentation Object +2

Towards Learning Monocular 3D Object Localization From 2D Labels using the Physical Laws of Motion

1 code implementation26 Oct 2023 Daniel Kienzle, Julian Lorenz, Katja Ludwig, Rainer Lienhart

We present a novel method for precise 3D object localization in single images from a single calibrated camera using only 2D labels.

Monocular 3D Object Localization Object

Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes

no code implementations5 Sep 2023 Julian Lorenz, Florian Barthel, Daniel Kienzle, Rainer Lienhart

We construct a new panoptic scene graph dataset and a set of metrics that are designed as a benchmark for the predictive performance especially on rare predicate classes.

Graph Generation Scene Graph Generation

Detecting Arbitrary Keypoints on Limbs and Skis with Sparse Partly Correct Segmentation Masks

1 code implementation17 Nov 2022 Katja Ludwig, Daniel Kienzle, Julian Lorenz, Rainer Lienhart

We analyze different training techniques for freely selected and standard keypoints, including pseudo labels, and show in our experiments that only a few partly correct segmentation masks are sufficient for learning to detect arbitrary keypoints on limbs and skis.

Segmentation

COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings

1 code implementation30 Jun 2022 Daniel Kienzle, Julian Lorenz, Robin Schön, Katja Ludwig, Rainer Lienhart

We introduce a neural network for the prediction of the severity of lung damage and the detection of a COVID-infection using three-dimensional CT-data.

severity prediction

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