13 citations · 19 across the 25 of their papers we have counts for
29 papers
FoundYou: A Unified Model for Personalized Segmentation and Retrieval
Gabriele Trivigno, Marcos Alfaro, Claudia Cuttano +3
Personalized segmentation and personalized retrieval both aim to identify the same physical object across different images. While the former localizes the object within a target im…
PrAda: Few-Shot Visual Adaptation for Text-Prompted Segmentation
Gabriele Rosi, Fabio Cermelli, Carlo Masone +1
Segmenting images is critical for visual understanding but demands extensive pixel-level annotations. Foundational models have enabled new paradigms for predicting new classes guid…
MARCO: Navigating the Unseen Space of Semantic Correspondence
Claudia Cuttano, Gabriele Trivigno, Carlo Masone +1
Recent advances in semantic correspondence rely on dual-encoder architectures, combining DINOv2 with diffusion backbones. While accurate, these billion-parameter models generalize…
INSID3: Training-Free In-Context Segmentation with DINOv3
Claudia Cuttano, Gabriele Trivigno, Christoph Reich +3
In-context segmentation (ICS) aims to segment arbitrary concepts, e.g., objects, parts, or personalized instances, given one annotated visual examples. Existing work relies on (i)…
Towards Safer and Understandable Driver Intention Prediction
Mukilan Karuppasamy, Shankar Gangisetty, Shyam Nandan Rai +2
Autonomous driving (AD) systems are becoming increasingly capable of handling complex tasks, mainly due to recent advances in deep learning and AI. As interactions between autonomo…
Road Obstacle Video Segmentation
Shyam Nandan Rai, Shyamgopal Karthik, Mariana-Iuliana Georgescu +3
With the growing deployment of autonomous driving agents, the detection and segmentation of road obstacles have become critical to ensure safe autonomous navigation. However, exist…
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…
To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition
Davide Sferrazza, Gabriele Berton, Gabriele Trivigno +1
Visual Place Recognition (VPR) is a critical task in computer vision, traditionally enhanced by re-ranking retrieval results with image matching. However, recent advancements in VP…
All You Need to Know About Training Image Retrieval Models
Gabriele Berton, Kevin Musgrave, Carlo Masone
Image retrieval is the task of finding images in a database that are most similar to a given query image. The performance of an image retrieval pipeline depends on many training-ti…
MegaLoc: One Retrieval to Place Them All
Gabriele Berton, Carlo Masone
Retrieving images from the same location as a given query is an important component of multiple computer vision tasks, like Visual Place Recognition, Landmark Retrieval, Visual Loc…
AstroLoc: Robust Space to Ground Image Localizer
Gabriele Berton, Alex Stoken, Carlo Masone
Astronauts take thousands of photos of Earth per day from the International Space Station, which, once localized on Earth's surface, are used for a multitude of tasks, ranging from…
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…
MeshVPR: Citywide Visual Place Recognition Using 3D Meshes
Gabriele Berton, Lorenz Junglas, Riccardo Zaccone +3
Mesh-based scene representation offers a promising direction for simplifying large-scale hierarchical visual localization pipelines, combining a visual place recognition step based…
Scale-Free Image Keypoints Using Differentiable Persistent Homology
Giovanni Barbarani, Francesco Vaccarino, Gabriele Trivigno +3
In computer vision, keypoint detection is a fundamental task, with applications spanning from robotics to image retrieval; however, existing learning-based methods suffer from scal…
EarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut Photography
Gabriele Berton, Gabriele Goletto, Gabriele Trivigno +3
Precise, pixel-wise geolocalization of astronaut photography is critical to unlocking the potential of this unique type of remotely sensed Earth data, particularly for its use in d…
Collaborative Visual Place Recognition through Federated Learning
Mattia Dutto, Gabriele Berton, Debora Caldarola +3
Visual Place Recognition (VPR) aims to estimate the location of an image by treating it as a retrieval problem. VPR uses a database of geo-tagged images and leverages deep neural n…
The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement
Gabriele Trivigno, Carlo Masone, Barbara Caputo +1
Pose refinement is an interesting and practically relevant research direction. Pose refinement can be used to (1) obtain a more accurate pose estimate from an initial prior (e.g.,…
JIST: Joint Image and Sequence Training for Sequential Visual Place Recognition
Gabriele Berton, Gabriele Trivigno, Barbara Caputo +1
Visual Place Recognition aims at recognizing previously visited places by relying on visual clues, and it is used in robotics applications for SLAM and localization. Since typicall…
EarthLoc: Astronaut Photography Localization by Indexing Earth from Space
Gabriele Berton, Alex Stoken, Barbara Caputo +1
Astronaut photography, spanning six decades of human spaceflight, presents a unique Earth observations dataset with immense value for both scientific research and disaster response…
EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition
Gabriele Berton, Gabriele Trivigno, Barbara Caputo +1
Visual Place Recognition is a task that aims to predict the place of an image (called query) based solely on its visual features. This is typically done through image retrieval, wh…
Unmasking Anomalies in Road-Scene Segmentation
Shyam Nandan Rai, Fabio Cermelli, Dario Fontanel +2
Anomaly segmentation is a critical task for driving applications, and it is approached traditionally as a per-pixel classification problem. However, reasoning individually about ea…
Divide&Classify: Fine-Grained Classification for City-Wide Visual Place Recognition
Gabriele Trivigno, Gabriele Berton, Juan Aragon +2
Visual Place recognition is commonly addressed as an image retrieval problem. However, retrieval methods are impractical to scale to large datasets, densely sampled from city-wide…
Are Local Features All You Need for Cross-Domain Visual Place Recognition?
Giovanni Barbarani, Mohamad Mostafa, Hajali Bayramov +4
Visual Place Recognition is a task that aims to predict the coordinates of an image (called query) based solely on visual clues. Most commonly, a retrieval approach is adopted, whe…
Hierarchical Instance Mixing across Domains in Aerial Segmentation
Edoardo Arnaudo, Antonio Tavera, Fabrizio Dominici +2
We investigate the task of unsupervised domain adaptation in aerial semantic segmentation and discover that the current state-of-the-art algorithms designed for autonomous driving…