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Found 19 papers, 14 papers with code
FusionINN: Decomposable Image Fusion for Brain Tumor Monitoring
Image fusion typically employs non-invertible neural networks to merge multiple source images into a single fused image.
Quantile-based Maximum Likelihood Training for Outlier Detection
Previous attempts to address this challenge involved training image classifiers through contrastive learning using actual outlier data or synthesizing outliers for self-supervised learning.
Learning to reconstruct the bubble distribution with conductivity maps using Invertible Neural Networks and Error Diffusion
Electrolysis is crucial for eco-friendly hydrogen production, but gas bubbles generated during the process hinder reactions, reduce cell efficiency, and increase energy consumption.
Towards Computational Performance Engineering for Unsupervised Concept Drift Detection -- Complexities, Benchmarking, Performance Analysis
Hence, we propose and explain performance engineering for unsupervised concept drift detection that reflects on computational complexities, benchmarking, and performance analysis.
Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection
However, this strategy does not guarantee that the synthesized outlier features will have a low likelihood according to the other class-conditional Gaussians.
Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration
Learning universal representations across different applications domain is an open research problem.
Ranked #1 on
Point Cloud Registration
on FPv1
Enhancing Fairness of Visual Attribute Predictors
The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes.
InFlow: Robust outlier detection utilizing Normalizing Flows
Normalizing flows are prominent deep generative models that provide tractable probability distributions and efficient density estimation.
FuseVis: Interpreting neural networks for image fusion using per-pixel saliency visualization
However, it is challenging to analyze the reliability of these CNNs for the image fusion tasks since no groundtruth is available.
Visualisation of Medical Image Fusion and Translation for Accurate Diagnosis of High Grade Gliomas
The medical image fusion combines two or more modalities into a single view while medical image translation synthesizes new images and assists in data augmentation.
Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task
We address a core problem of computer vision: Detection and description of 2D feature points for image matching.
Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift
In order to work with wide-baseline light fields, we introduce the idea of EPI-Shift: To virtually shift the light field stack which enables to retain a small receptive field, independent of the disparity range.
Structural Similarity based Anatomical and Functional Brain Imaging Fusion
Multimodal medical image fusion helps in combining contrasting features from two or more input imaging modalities to represent fused information in a single image.
scenery -- Flexible Virtual Reality Visualisation on the Java VM
Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data resulting from analysis of such data or simulations.
Graphics
Global Hypothesis Generation for 6D Object Pose Estimation
Most modern approaches solve this task in three steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii) Select and refine a pose from the pool.
In situ, steerable, hardware-independent and data-structure agnostic visualization with ISAAC
The computation power of supercomputers grows faster than the bandwidth of their storage and network.
Distributed, Parallel, and Cluster Computing
DSAC - Differentiable RANSAC for Camera Localization
The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w. r. t.
Uncertainty-Driven 6D Pose Estimation of Objects and Scenes From a Single RGB Image
In recent years, the task of estimating the 6D pose of object instances and complete scenes, i. e. camera localization, from a single input image has received considerable attention.
Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images
This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares an observed and rendered image.
