Search Results for author:
Found 12 papers, 4 papers with code
The impact of AI on engineering design procedures for dynamical systems
Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation.
Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and Examples from Engineering Design
Data-based methods have gained increasing importance in engineering, especially but not only driven by successes with deep artificial neural networks.
Computer Vision for Primate Behavior Analysis in the Wild
We start with a survey of the state-of-the-art methods for computer vision problems that are directly relevant to the video-based study of animal behavior, including object detection, multi-individual tracking, individual identification, and (inter)action recognition.
Charting New Territories: Exploring the Geographic and Geospatial Capabilities of Multimodal LLMs
Multimodal large language models (MLLMs) have shown remarkable capabilities across a broad range of tasks but their knowledge and abilities in the geographic and geospatial domains are yet to be explored, despite potential wide-ranging benefits to navigation, environmental research, urban development, and disaster response.
Learning to Predict Structural Vibrations
To quantify such trade-offs systematically and foster the development of methods, we present a benchmark on the task of predicting the vibration of harmonically excited plates.
GPT4GEO: How a Language Model Sees the World's Geography
Large language models (LLMs) have shown remarkable capabilities across a broad range of tasks involving question answering and the generation of coherent text and code.
Self-Supervised Graph Representation Learning for Neuronal Morphologies
GraphDINO is a novel transformer-based representation learning method for spatially-embedded graphs.
Image Segmentation Using Text and Image Prompts
After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query.
Ranked #3 on
Referring Image Matting (Keyword-based)
on RefMatte
CNNs efficiently learn long-range dependencies
The role of feedback (or recurrent) connections is a fundamental question in neuroscience and machine learning.
One-shot path planning for multi-agent systems using fully convolutional neural network
In contrast to that, we propose a novel method by utilising fully convolutional neural network, which allows generation of complete paths, even for more than one agent, in one-shot, i. e., with a single prediction step.
Attention on Abstract Visual Reasoning
Our proposed hybrid model, represents an alternative on learning abstract relations using self-attention and demonstrates that the Transformer network is also well suited for abstract visual reasoning.
Learning to Label Affordances from Simulated and Real Data
An autonomous robot should be able to evaluate the affordances that are offered by a given situation.
