Keuper, J.[Janis]
* 2019: Object Segmentation Using Pixel-Wise Adversarial Loss
* 2019: Semi Few-Shot Attribute Translation
* 2020: Two-stage Minimum Cost Multicut Approach to Self-supervised Multiple Person Tracking, A
* 2020: Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions
* 2021: Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches
* 2021: Local Facial Attribute Transfer through Inpainting
* 2022: Adversarial Robustness through the Lens of Convolutional Filters
* 2022: CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters
* 2022: FrequencyLowCut Pooling: Plug and Play Against Catastrophic Overfitting
* 2023: Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality
* 2023: Extended Study of Human-like Behavior under Adversarial Training, An
* 2023: On the Interplay of Convolutional Padding and Adversarial Robustness
Includes: Keuper, J.[Janis] Keuper, J.
12 for Keuper, J.
Keuper, M.[Margret]
* 2009: 3D Active Surface Model for the Accurate Segmentation of Drosophila Schneider Cell Nuclei and Nucleoli, A
* 2010: 3D Deformable Surfaces with Locally Self-Adjusting Parameters: A Robust Method to Determine Cell Nucleus Shapes
* 2010: Evaluation of a New Point Clouds Registration Method Based on Group Averaging Features
* 2010: Mean Shift Gradient Vector Flow: A Robust External Force Field for 3D Active Surfaces
* 2010: Semi-supervised Learning of Edge Filters for Volumetric Image Segmentation
* 2012: Modeling of Sparsely Sampled Tubular Surfaces Using Coupled Curves
* 2013: Blind Deconvolution of Widefield Fluorescence Microscopic Data by Regularization of the Optical Transfer Function (OTF)
* 2014: Spectral Graph Reduction for Efficient Image and Streaming Video Segmentation
* 2015: Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts
* 2015: Motion Trajectory Segmentation via Minimum Cost Multicuts
* 2016: Point-Wise Mutual Information-Based Video Segmentation with High Temporal Consistency
* 2017: FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
* 2017: Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentation
* 2017: Learning Dilation Factors for Semantic Segmentation of Street Scenes
* 2017: ProbFlow: Joint Optical Flow and Uncertainty Estimation
* 2017: STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling
* 2018: Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation
* 2018: Solving Minimum Cost Lifted Multicut Problems by Node Agglomeration
* 2020: Motion Segmentation Multiple Object Tracking by Correlation Co-Clustering
* 2020: Neural Architecture Performance Prediction Using Graph Neural Networks
* 2020: Self-supervised Sparse to Dense Motion Segmentation
* 2020: Two-stage Minimum Cost Multicut Approach to Self-supervised Multiple Person Tracking, A
* 2020: Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions
* 2021: Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches
* 2021: Multi-Class Multi-Instance Count Conditioned Adversarial Image Generation
* 2021: Object Segmentation Tracking from Generic Video Cues
* 2022: FrequencyLowCut Pooling: Plug and Play Against Catastrophic Overfitting
* 2022: Learning Where to Look: Generative NAS is Surprisingly Efficient
* 2023: Classification robustness to common optical aberrations
* 2023: Extended Study of Human-like Behavior under Adversarial Training, An
* 2023: Higher-Order Multicuts for Geometric Model Fitting and Motion Segmentation
* 2023: Intra-Source Style Augmentation for Improved Domain Generalization
* 2023: On the unreasonable vulnerability of transformers for image restoration - and an easy fix
* 2024: Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalization
Includes: Keuper, M.[Margret] Keuper, M.
34 for Keuper, M.
Keupers, M.[Machteld]
* 2019: statistical evaluation of eye-tracking data of screening mammography: Effects of expertise and experience on image reading, A