Search Results for author:
Found 25 papers, 4 papers with code
AIT_FHSTP at GermEval 2021: Automatic Fact Claiming Detection with Multilingual Transformer Models
Our best model is GottBERT (i. e., a BERT transformer pre-trained on German texts) fine-tuned on the GermEval 2021 data.
Case Study: Ensemble Decision-Based Annotation of Unconstrained Real Estate Images
We describe a proof-of-concept for annotating real estate images using simple iterative rule-based semi-supervised learning.
Explaining YOLO: Leveraging Grad-CAM to Explain Object Detections
We investigate the problem of explainability for visual object detectors.
Real Estate Attribute Prediction from Multiple Visual Modalities with Missing Data
Furthermore, the fusion of information from indoor and outdoor photos results in a performance boost of up to 5% in Macro F1-score.
Explaining automated gender classification of human gait
State-of-the-art machine learning (ML) models are highly effective in classifying gait analysis data, however, they lack in providing explanations for their predictions.
Explaining machine learning models for age classification in human gait analysis
Machine learning (ML) models have proven effective in classifying gait analysis data, e. g., binary classification of young vs. older adults.
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy
Three-dimensional clinical gait analysis is essential for selecting optimal treatment interventions for patients with cerebral palsy (CP), but generates a large amount of time series data.
Automatic Sexism Detection with Multilingual Transformer Models
For both tasks our best model is XLM-R with unsupervised pre-training on the EXIST data and additional datasets and fine-tuning on the provided dataset.
Multimodal Detection of Information Disorder from Social Media
Social media is accompanied by an increasing proportion of content that provides fake information or misleading content, known as information disorder.
Bounded logit attention: Learning to explain image classifiers
Explainable artificial intelligence is the attempt to elucidate the workings of systems too complex to be directly accessible to human cognition through suitable side-information referred to as "explanations".
$k$-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers
For use with anonymisation techniques, the $k$-anonymity criterion is one of the most popular, with numerous scientific publications on different algorithms and metrics.
Machine Unlearning: Linear Filtration for Logit-based Classifiers
Recently enacted legislation grants individuals certain rights to decide in what fashion their personal data may be used, and in particular a "right to be forgotten".
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis
Machine learning (ML) is increasingly used to support decision-making in the healthcare sector.
Persistence Bag-of-Words for Topological Data Analysis
Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs).
Visual Estimation of Building Condition with Patch-level ConvNets
The condition of a building is an important factor for real estate valuation.
Automatic Prediction of Building Age from Photographs
We present a first method for the automated age estimation of buildings from unconstrained photographs.
Persistence Codebooks for Topological Data Analysis
Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs) which are 2D multisets of points.
Automatic Classification of Functional Gait Disorders
The aim of the study is twofold: (1) to investigate the suitability of stateof-the-art GRF parameterization techniques (representations) for the discrimination of functional gait disorders; and (2) to provide a first performance baseline for the automated classification of functional gait disorders for a large-scale dataset.
A Study on Topological Descriptors for the Analysis of 3D Surface Texture
Methods from computational topology are becoming more and more popular in computer vision and have shown to improve the state-of-the-art in several tasks.
Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis
In a visual-interactive interface, users are able to label pairs of soccer players with respect to their subjective notion of similarity.
PetroSurf3D - A Dataset for high-resolution 3D Surface Segmentation
The development of powerful 3D scanning hardware and reconstruction algorithms has strongly promoted the generation of 3D surface reconstructions in different domains.
Topological descriptors for 3D surface analysis
We investigate topological descriptors for 3D surface analysis, i. e. the classification of surfaces according to their geometric fine structure.
Multimodal Classification of Events in Social Media
The task of social event classification refers to the distinction of event and non-event-related content as well as the classification of event types (e. g. sports events, concerts, etc.).
Efficient Image-Space Extraction and Representation of 3D Surface Topography
Surface topography refers to the geometric micro-structure of a surface and defines its tactile characteristics (typically in the sub-millimeter range).
Cultural Event Recognition with Visual ConvNets and Temporal Models
Our solution is based on the combination of visual features extracted from convolutional neural networks with temporal information using a hierarchical classifier scheme.
