68 citations · 73 across the 9 of their papers we have counts for
9 papers
GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models
Garvita Allabadi, Matteo Sodano, Roberto Estevão +4
In-Context Learning (ICL) has become a powerful mechanism for adapting Large Language Models (LLMs) to new tasks without fine-tuning. Extending this concept to Large Multimodal Mod…
3D Hierarchical Panoptic Segmentation in Real Orchard Environments Across Different Sensors
Matteo Sodano, Federico Magistri, Elias Marks +6
Crop yield estimation is a relevant problem in agriculture, because an accurate yield estimate can support farmers' decisions on harvesting or precision intervention. Robots can he…
Open-World Panoptic Segmentation
Matteo Sodano, Federico Magistri, Jens Behley +1
Robots need to be able to understand their surroundings in order to operate safely and robustly, and to interact with the surrounding environment. Robots deployed in unconstrained…
Human-Inspired Long-Term Indoor Localization in Human-Oriented Environment
Nicky Zimmerman, Matteo Sodano
Lifelong localization is crucial for enabling the autonomy of service robots. In this paper, we present an overview of our past research on long-term localization and mapping, expl…
Open-World Semantic Segmentation Including Class Similarity
Matteo Sodano, Federico Magistri, Lucas Nunes +2
Interpreting camera data is key for autonomously acting systems, such as autonomous vehicles. Vision systems that operate in real-world environments must be able to understand thei…
PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain
Jan Weyler, Federico Magistri, Elias Marks +6
The production of food, feed, fiber, and fuel is a key task of agriculture, which has to cope with many challenges in the upcoming decades, e.g., a higher demand, climate change, l…
Constructing Metric-Semantic Maps using Floor Plan Priors for Long-Term Indoor Localization
Nicky Zimmerman, Matteo Sodano, Elias Marks +2
Object-based maps are relevant for scene understanding since they integrate geometric and semantic information of the environment, allowing autonomous robots to robustly localize a…
Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain
Gianmarco Roggiolani, Matteo Sodano, Tiziano Guadagnino +3
Plant phenotyping is a central task in agriculture, as it describes plants' growth stage, development, and other relevant quantities. Robots can help automate this process by accur…
Robust Double-Encoder Network for RGB-D Panoptic Segmentation
Matteo Sodano, Federico Magistri, Tiziano Guadagnino +2
Perception is crucial for robots that act in real-world environments, as autonomous systems need to see and understand the world around them to act properly. Panoptic segmentation…