Our work Compensation Learning in Semantic Segmentation will be presented at The 2nd Workshop Challenge on Vision Datasets Understanding (VDU) at The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (CVPR)
Our work PEKORA: High-Performance 3D Genome Reconstruction Using K-th Order Spearman's Rank Correlation Approximation will be presented at HiTSeq COSI track at ISMB/ECCB 2023
Our work POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning has been accepted at the Transactions on Machine Learning Research (TMLR)!
We are pleased to announce that our work RelTR: Relation Transformer for Scene Graph Generation in collaboration with SUG, University of Twente, has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). The code is available on Github.
We are happy to announce that our work in collaboration with IENT RWTH Aachen titled GVC: Efficient Random Access Compression for Gene Sequence Variations has been published in the journal BMC Bioinformatics. The code is available on GitHub.
The TNT is excited to announce that we are part of the Horizon Europe funded project Satellites for Wilderness Inspection and Forest Threat Tracking (SWIFTT). The project's goal is to use machine learning to enable affordable, simple, and early forest threat detection. SWIFTT's first press release can be found here.
Our paper Deep Reinforcement Learning for Autonomous Driving using High-Level Heterogeneous Graph Representations has been accepted to ICRA 2023! The accepted version is available here, code will be published shortly.
The “Institut für Informationsverarbeitung” (Institute for Information Processing) is the home of machine learning at the Leibniz University Hannover. We focus on three main research directions, namely (I) computer vision & representation learning, (II) signal processing & -coding and (III) automated machine learning. Our methods range from deep learning, automated machine learning, reinforcement learning, image analysis, remote sensing and compression of audio, image, video as well as DNA to biomedical data. Our efforts are directed towards making efficient use of multi-modal and high dimensional data for reliable predictions, ultimately supporting end-users, developers and decision makers in a vast range of applications.
Since the foundation in 1973, the institute holds a strong tradition of cultivating connections to industry partners and jointly developing solutions to automatically process and harness information. Some of the developed methodology were successfully spun-off commercially as for instance with driver assistance modules, cochlear implants or component testing. The institute is also well known for being actively involved in the standardization of MP3, MPEG-2, AVC (H.264), HEVC (H.265) as well as MPEG-G.
Do you want to join us? We have open positions.