1 citations · 4 across the 23 of their papers we have counts for
25 papers
GORDON: Graph-based Object-centric Rewards for Decomposition of Long-Horizon Manipulation
Andrea Protopapa, Davide Buoso, Francesca Pistilli +2
Learning long-horizon manipulation skills with reinforcement learning remains challenging due to the complexity of reward design, the limited guidance of sparse rewards, and the hi…
Learning to Evolve Scenes: Reasoning about Human Activities with Scene Graphs
Francesca Pistilli, Simone Alberto Peirone, Giuseppe Averta
Understanding human behavior while interacting with the surrounding world is crucial for many applications of embodied AI. First-person videos are particularly informative for this…
HiERO-StepG @ Ego4D Step Grounding Challenge: hierarchical activity understanding enables zero-shot step grounding
Andrea Zenotto, Simone Alberto Peirone, Francesca Pistilli +1
Procedural activities follow well-defined structures: whether we consider a cooking recipe or a mechanic repairing a car, these activities naturally decompose in a hierarchy of ste…
RGB-only Active 3D Scene Graph Generation for Indoor Mobile Robots
Giorgia Modi, Davide Buoso, Giuseppe Averta +1
Current approaches to 3D scene graph generation rely on dedicated depth sensors, such as LiDAR or RGB-D cameras, for metric 3D reconstruction. This limits deployment to specialized…
Fixed External Cameras as Common Prior Maps for Active 3D Scene Graph Generation
Giorgia Modi, Davide Buoso, Giuseppe Averta +1
Commonly available prior information, such as BIM models, floor plans, and remote sensing images, can provide valuable geometric and semantic context for autonomous robotic systems…
GAP: Geometric Anchor Pre-training for Data-Efficient Visuomotor Learning of Manipulation Tasks
Davide Buoso, Andrea Protopapa, Stefano Di Carlo +2
Learning visuomotor policies from scarce expert demonstrations remains a core challenge in robotic manipulation. A primary hurdle lies in distilling high-dimensional RGB representa…
Domain Generalization using Action Sequences for Egocentric Action Recognition
Amirshayan Nasirimajd, Chiara Plizzari, Simone Alberto Peirone +3
Recognizing human activities from visual inputs, particularly through a first-person viewpoint, is essential for enabling robots to replicate human behavior. Egocentric vision, cha…
Learning reusable concepts across different egocentric video understanding tasks
Simone Alberto Peirone, Francesca Pistilli, Antonio Alliegro +2
Our comprehension of video streams depicting human activities is naturally multifaceted: in just a few moments, we can grasp what is happening, identify the relevance and interacti…
HiERO: understanding the hierarchy of human behavior enhances reasoning on egocentric videos
Simone Alberto Peirone, Francesca Pistilli, Giuseppe Averta
Human activities are particularly complex and variable, and this makes challenging for deep learning models to reason about them. However, we note that such variability does have a…
SANSA: Unleashing the Hidden Semantics in SAM2 for Few-Shot Segmentation
Claudia Cuttano, Gabriele Trivigno, Giuseppe Averta +1
Few-shot segmentation aims to segment unseen object categories from just a handful of annotated examples. This requires mechanisms that can both identify semantically related objec…
Searching on a Budget: HW-NAS with 10 Latency Probes
Francesco Capuano, Gabriele Tiboni, Niccolò Cavagnero +1
Existing hardware-aware NAS (HW-NAS) methods typically assume access to precise information circa the target device, either via analytical approximations of the post-compilation la…
Your ViT is Secretly an Image Segmentation Model
Tommie Kerssies, Niccolò Cavagnero, Alexander Hermans +5
Vision Transformers (ViTs) have shown remarkable performance and scalability across various computer vision tasks. To apply single-scale ViTs to image segmentation, existing method…
FORESCENE: FOREcasting human activity via latent SCENE graphs diffusion
Antonio Alliegro, Francesca Pistilli, Tatiana Tommasi +1
Forecasting human-environment interactions in daily activities is challenging due to the high variability of human behavior. While predicting directly from videos is possible, it i…
Hier-EgoPack: Hierarchical Egocentric Video Understanding with Diverse Task Perspectives
Simone Alberto Peirone, Francesca Pistilli, Antonio Alliegro +2
Our comprehension of video streams depicting human activities is naturally multifaceted: in just a few moments, we can grasp what is happening, identify the relevance and interacti…
Select2Plan: Training-Free ICL-Based Planning through VQA and Memory Retrieval
Davide Buoso, Luke Robinson, Giuseppe Averta +3
This study explores the potential of off-the-shelf Vision-Language Models (VLMs) for high-level robot planning in the context of autonomous navigation. Indeed, while most of existi…
SAMWISE: Infusing Wisdom in SAM2 for Text-Driven Video Segmentation
Claudia Cuttano, Gabriele Trivigno, Gabriele Rosi +2
Referring Video Object Segmentation (RVOS) relies on natural language expressions to segment an object in a video clip. Existing methods restrict reasoning either to independent sh…
Egocentric zone-aware action recognition across environments
Simone Alberto Peirone, Gabriele Goletto, Mirco Planamente +3
Human activities exhibit a strong correlation between actions and the places where these are performed, such as washing something at a sink. More specifically, in daily living envi…
AMEGO: Active Memory from long EGOcentric videos
Gabriele Goletto, Tushar Nagarajan, Giuseppe Averta +1
Egocentric videos provide a unique perspective into individuals' daily experiences, yet their unstructured nature presents challenges for perception. In this paper, we introduce AM…
Transient Fault Tolerant Semantic Segmentation for Autonomous Driving
Leonardo Iurada, Niccolò Cavagnero, Fernando Fernandes Dos Santos +3
Deep learning models are crucial for autonomous vehicle perception, but their reliability is challenged by algorithmic limitations and hardware faults. We address the latter by exa…
What does CLIP know about peeling a banana?
Claudia Cuttano, Gabriele Rosi, Gabriele Trivigno +1
Humans show an innate capability to identify tools to support specific actions. The association between objects parts and the actions they facilitate is usually named affordance. B…
The revenge of BiSeNet: Efficient Multi-Task Image Segmentation
Gabriele Rosi, Claudia Cuttano, Niccolò Cavagnero +2
Recent advancements in image segmentation have focused on enhancing the efficiency of the models to meet the demands of real-time applications, especially on edge devices. However,…
A Backpack Full of Skills: Egocentric Video Understanding with Diverse Task Perspectives
Simone Alberto Peirone, Francesca Pistilli, Antonio Alliegro +1
Human comprehension of a video stream is naturally broad: in a few instants, we are able to understand what is happening, the relevance and relationship of objects, and forecast wh…
PEM: Prototype-based Efficient MaskFormer for Image Segmentation
Niccolò Cavagnero, Gabriele Rosi, Claudia Cuttano +4
Recent transformer-based architectures have shown impressive results in the field of image segmentation. Thanks to their flexibility, they obtain outstanding performance in multipl…
Graph learning in robotics: a survey
Francesca Pistilli, Giuseppe Averta
Deep neural networks for graphs have emerged as a powerful tool for learning on complex non-euclidean data, which is becoming increasingly common for a variety of different applica…