3 citations · 4 across the 16 of their papers we have counts for
19 papers
Scalable Patch-Level Self-Supervised Learning
Maximilian Seitzer, Gabriele Trivigno, Antonín Vobecký +5
Self-supervised learning (SSL) at scale produces powerful visual representations. However, most scalable SSL methods rely on ad hoc combinations of multiple objectives and stabiliz…
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…
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)…
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…
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…
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…
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 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…
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…
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…
PoliTO-IIT-CINI Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition
Mirco Planamente, Gabriele Goletto, Gabriele Trivigno +2
In this report, we describe the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition. To tackle the dom…
Learning Sequential Descriptors for Sequence-based Visual Place Recognition
Riccardo Mereu, Gabriele Trivigno, Gabriele Berton +2
In robotics, Visual Place Recognition is a continuous process that receives as input a video stream to produce a hypothesis of the robot's current position within a map of known pl…
Deep Visual Geo-localization Benchmark
Gabriele Berton, Riccardo Mereu, Gabriele Trivigno +4
In this paper, we propose a new open-source benchmarking framework for Visual Geo-localization (VG) that allows to build, train, and test a wide range of commonly used architecture…