Search Results for author:
Found 13 papers, 8 papers with code
Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation
We demonstrate the usage of state-of-the-art text-to-image architectures in the context of laparoscopic imaging with regard to the surgical removal of the gallbladder as an example.
Redefining the Laparoscopic Spatial Sense: AI-based Intra- and Postoperative Measurement from Stereoimages
A significant challenge in image-guided surgery is the accurate measurement task of relevant structures such as vessel segments, resection margins, or bowel lengths.
Foundation Models for Generalist Geospatial Artificial Intelligence
This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive geospatial data.
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation
In particular, we are witnessing the rise of AI foundation models that can perform competitively on multiple domain-specific downstream tasks.
Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts
Hence, human-in-the-loop (HITL) extensions to ML models add a human review for instances that are difficult to classify.
What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation
To address this problem, zero-shot semantic segmentation makes use of large self-supervised vision-language models, allowing zero-shot transfer to unseen classes.
On the Interdependence of Reliance Behavior and Accuracy in AI-Assisted Decision-Making
In AI-assisted decision-making, a central promise of putting a human in the loop is that they should be able to complement the AI system by adhering to its correct and overriding its mistaken recommendations.
Learning to Defer with Limited Expert Predictions
In this paper, we propose a three-step approach to reduce the number of expert predictions required to train learning to defer algorithms.
Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation
However, DL-based approaches are designed for one specific task in a single geographic region based on specific frequency bands of satellite data.
Data-Centric Artificial Intelligence
Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm emphasizing that the systematic design and engineering of data is essential for building effective and efficient AI-based systems.
Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot Learning
Few-shot learning addresses this challenge and reduces data gathering and labeling costs by learning novel classes with very few labeled data.
Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts
In this work, we propose an approach that trains a classification model to complement the capabilities of multiple human experts.
Directed particle swarm optimization with Gaussian-process-based function forecasting
Particle swarm optimization (PSO) is an iterative search method that moves a set of candidate solution around a search-space towards the best known global and local solutions with randomized step lengths.
