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Found 10 papers, 3 papers with code
Computer Vision for Primate Behavior Analysis in the Wild
With this perspective paper, we want to contribute towards closing this gap, by guiding behavioral scientists in what can be expected from current methods and steering computer vision researchers towards problems that are relevant to advance research in animal behavior.
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.
