Sept. 5, 2023

Paper for SG2RL @ ICCV 2023 accepted

The paper “Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes” by Julian Lorenz, Florian Barthel, Daniel Kienzle, and Rainer Lienhart is accepted at the First ICCV Workshop on Scene Graphs and Graph Representation Learning (SG2RL). The authors present Haystack, a new dataset for scene graph generation that tackles current shortcomings when evaluating with current scene graph datasets. Most notably, Haystack contains rare predicate classes and explicit negative annotations. Only through these properties can rare relationships be reliably evaluated. Based on the design of Haystack, the authors introduce three new scene graph metrics that can be used to gain more detailed insights about the prediction of rare predicate classes.

Read more
A diagram that highlights the differences of Haystack and the PSG dataset. Haystack does not contain false negative annotations.
April 12, 2023

Paper for L3D-IVU @ CVPR 2023 accepted

A paper with the title "Impact of Pseudo Depth on Open World Object Segmentation with Minimal User Guidance" by Robin Schön, Katja Ludwig and Rainer Lienhart has been accepted to the  2nd Workshop on Learning with Limited Labelled Data for Image and Video Understanding at the CVPR 2023. In this paper, the authors examine the effect of pseudo depth maps on the segmentation of object types which have not been present in the training data. The targeted objects are indicated by the means of coordinates on the object surfaces. In order to avoid a dependency ground truth depth maps, the depth maps are predicted by networks.
Read more
Dieses Bild visualisiert die Segmentierung eines angeklickten Objekts auf Tiefenkarten.
April 5, 2023

Paper for CVSports @ CVPR 2023 accepted

The paper with the title "All Keypoints You Need: Detecting Arbitrary Keypoints on the Body of Triple, High, and Long Jump Athletes" by Katja Ludwig, Julian Lorenz, Robin Schön and Rainer Lienhart is accepted at the 9th International Workshop on Computer Vision in Sports (CVsports) at CVPR 2023. In this paper, the authors detect arbitrary keypoints on the body of triple, high, and long jump athletes by extending previous methods with detections on the hands, feet, heads, elbows, and knees. Different representations regarding the head and the network input are evaluated in the paper.
Read more
Example image from detection model showing body outline and intermediate lines



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-



© University of Augsburg