Machine Learning & Computer Vision
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Paper accepted at International Conference on 3D Vision (3DV) 2024
The paper titled "Towards Learning Monocular 3D Object Localization Using the Physical Laws of Motion" by Daniel Kienzle, Julian Lorenz, Katja Ludwig and Rainer Lienhart was accepted to the International Conference on 3D Vision (3DV) 2024. The paper describes a new method for localizing objects in 3D without the need for 3D ground truth. Instead, the method uses knowledge of physical laws to learn the task.
See https://kiedani.github.io/3DV2024/ for additional information on the paper.
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.
Contact
Address
Prof. Dr. Rainer Lienhart
Lehrstuhl für Maschinelles Lernen und Maschinelles Sehen
Institut für Informatik
Universität Augsburg
Universitätsstr. 6a
D -
89159 AugsburgGermany
Phone
+49 (821) 598-5703
rainer.lienhart @informatik.uni- augsburg.de