Search Results for author:
Found 18 papers, 6 papers with code
Evaluating the Efficacy of Cut-and-Paste Data Augmentation in Semantic Segmentation for Satellite Imagery
Satellite imagery is crucial for tasks like environmental monitoring and urban planning.
LLM Based Multi-Agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain
In the last years' digitalization process, the creation and management of documents in various domains, particularly in Public Administration (PA), have become increasingly complex and diverse.
AgriSORT: A Simple Online Real-time Tracking-by-Detection framework for robotics in precision agriculture
The problem of multi-object tracking (MOT) consists in detecting and tracking all the objects in a video sequence while keeping a unique identifier for each object.
Enhancing Graph Representation of the Environment through Local and Cloud Computation
Enriching the robot representation of the operational environment is a challenging task that aims at bridging the gap between low-level sensor readings and high-level semantic understanding.
Weakly and Semi-Supervised Detection, Segmentation and Tracking of Table Grapes with Limited and Noisy Data
To improve detection and segmentation on the target data, we propose to train the segmentation algorithm with a weak bounding box label, while for tracking we leverage 3D Structure from Motion algorithms to generate new labels from already labelled samples.
A self-interpretable module for deep image classification on small data
This paper presents Memory Wrap, a module (i. e, a set of layers) that can be added to deep learning models to improve their performance and interpretability in settings where few data are available.
Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification Models
Due to their black-box and data-hungry nature, deep learning techniques are not yet widely adopted for real-world applications in critical domains, like healthcare and justice.
Towards Abstract Relational Learning in Human Robot Interaction
Entities are described by their attributes, and entities that share attributes are often semantically related.
Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming
In this work, we propose an alternative solution with respect to the common data augmentation methods, applying it to the fundamental problem of crop/weed segmentation in precision farming.
Explainable Inference on Sequential Data via Memory-Tracking
Our results show that we are able to explain agent’s decisions in (1) and to reconstruct the most relevant sentences used by the network to select the story ending in (2).
AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming
The combination of aerial survey capabilities of Unmanned Aerial Vehicles with targeted intervention abilities of agricultural Unmanned Ground Vehicles can significantly improve the effectiveness of robotic systems applied to precision agriculture.
DOP: Deep Optimistic Planning with Approximate Value Function Evaluation
To alleviate this problem, we present DOP, a deep model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) plan effective policies.
Model-based Reinforcement Learning
reinforcement-learning
+1
Q-CP: Learning Action Values for Cooperative Planning
Research on multi-robot systems has demonstrated promising results in manifold applications and domains.
Structured Learning for Context-aware Spoken Language Understanding of Robotic Commands
Service robots are expected to operate in specific environments, where the presence of humans plays a key role.
Learning Human-Robot Handovers Through $π$-STAM: Policy Improvement With Spatio-Temporal Affordance Maps
Human-robot handovers are characterized by high uncertainty and poor structure of the problem that make them difficult tasks.
Robotics
HuRIC: a Human Robot Interaction Corpus
Recent years show the development of large scale resources (e. g. FrameNet for the Frame Semantics) that supported the definition of several state-of-the-art approaches in Natural Language Processing.
Knowledge Representation for Robots through Human-Robot Interaction
The representation of the knowledge needed by a robot to perform complex tasks is restricted by the limitations of perception.
