activity
20142026
most citedAdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection

128 citations · 293 across the 39 of their papers we have counts for

collaborators
Showing 2025Show all

14 papers · 1 filter

cs.CV2025

One target to align them all: LiDAR, RGB and event cameras extrinsic calibration for Autonomous Driving

Andrea Bertogalli, Giacomo Boracchi, Luca Magri

We present a novel multi-modal extrinsic calibration framework designed to simultaneously estimate the relative poses between event cameras, LiDARs, and RGB cameras, with particula…

cs.CV2025

Convolutional Set Transformer

Federico Chinello, Giacomo Boracchi

We introduce the Convolutional Set Transformer (CST), a novel neural architecture designed to process image sets of arbitrary cardinality that are visually heterogeneous yet share…

cs.CV2025

WS: Weakly Supervised Segmentation using Before-After Supervision in Waste Sorting

Andrea Marelli, Alberto Foresti, Leonardo Pesce +2

In industrial quality control, to visually recognize unwanted items within a moving heterogeneous stream, human operators are often still indispensable. Waste-sorting stands as a s…

cs.RO2025

Biasing Frontier-Based Exploration with Saliency Areas

Matteo Luperto, Valerii Stakanov, Giacomo Boracchi +2

Autonomous exploration is a widely studied problem where a robot incrementally builds a map of a previously unknown environment. The robot selects the next locations to reach using…

cs.CR2025

Malware families discovery via Open-Set Recognition on Android manifest permissions

Filippo Leveni, Matteo Mistura, Francesco Iubatti +4

Malware are malicious programs that are grouped into families based on their penetration technique, source code, and other characteristics. Classifying malware programs into their…

cs.LG2025★ 20 cited

MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model Selection

Luca Magri, Filippo Leveni, Giacomo Boracchi

We address the problem of recovering multiple structures of different classes in a dataset contaminated by noise and outliers. In particular, we consider geometric structures defin…

cs.LG2025★ 1 cited

Hashing for Structure-based Anomaly Detection

Filippo Leveni, Luca Magri, Cesare Alippi +1

We focus on the problem of identifying samples in a set that do not conform to structured patterns represented by low-dimensional manifolds. An effective way to solve this problem…

cs.LG2025★ 3 cited

PIF: Anomaly detection via preference embedding

Filippo Leveni, Luca Magri, Giacomo Boracchi +1

We address the problem of detecting anomalies with respect to structured patterns. To this end, we conceive a novel anomaly detection method called PIF, that combines the advantage…

cs.LG2025

Online Isolation Forest

Filippo Leveni, Guilherme Weigert Cassales, Bernhard Pfahringer +2

The anomaly detection literature is abundant with offline methods, which require repeated access to data in memory, and impose impractical assumptions when applied to a streaming c…

cs.LG2025★ 5 cited

Preference Isolation Forest for Structure-based Anomaly Detection

Filippo Leveni, Luca Magri, Cesare Alippi +1

We address the problem of detecting anomalies as samples that do not conform to structured patterns represented by low-dimensional manifolds. To this end, we conceive a general ano…

cs.CV2025★ 8 cited

A Multimodal Hybrid Late-Cascade Fusion Network for Enhanced 3D Object Detection

Carlo Sgaravatti, Roberto Basla, Riccardo Pieroni +4

We present a new way to detect 3D objects from multimodal inputs, leveraging both LiDAR and RGB cameras in a hybrid late-cascade scheme, that combines an RGB detection network and…

eess.SY2025★ 13 cited

Adaptive Extended Kalman Filtering for Battery State of Charge Estimation on STM32

António Barros, Edoardo Peretti, Davide Fabroni +3

Accurate and computationally light algorithms for estimating the State of Charge (SoC) of a battery's cells are crucial for effective battery management on embedded systems. In thi…

cs.CV2025

Temporal-consistent CAMs for Weakly Supervised Video Segmentation in Waste Sorting

Andrea Marelli, Luca Magri, Federica Arrigoni +1

In industrial settings, weakly supervised (WS) methods are usually preferred over their fully supervised (FS) counterparts as they do not require costly manual annotations. Unfortu…

cs.CV2025★ 2 cited

A Deep Learning Pipeline for Solid Waste Detection in Remote Sensing Images

Federico Gibellini, Piero Fraternali, Giacomo Boracchi +4

Improper solid waste management represents both a serious threat to ecosystem health and a significant source of revenues for criminal organizations perpetrating environmental crim…