2 citations · 4 across the 8 of their papers we have counts for
11 papers · 1 filter
GigaBench: Benchmarking Learnable Representations for Gigapixel Image Compression and Super-Resolution
Matteo Fusconi, Alex Costanzino, Pierluigi Zama Ramirez +1
Learnable Representations (LRs) can fit parametric families of functions to diverse kinds of signals. These emergent neural-native data formats are gathering interest in the scient…
Spectral Gating via Damped Oscillations for Adaptive Implicit Neural Representations
Alex Costanzino, Pierluigi Zama Ramirez, Giuseppe Lisanti +1
Implicit Neural Representations (INRs) have been proven successful in encoding continuous signals through coordinate-based networks, yet facing a spectral dilemma: periodic activat…
Modulate-and-Map: Crossmodal Feature Mapping with Cross-View Modulation for 3D Anomaly Detection
Alex Costanzino, Pierluigi Zama Ramirez, Giuseppe Lisanti +1
We present ModMap, a natively multiview and multimodal framework for 3D anomaly detection and segmentation. Unlike existing methods that process views independently, our method dra…
SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark
Alex Costanzino, Pierluigi Zama Ramirez, Luigi Lella +4
We propose SiM3D, the first benchmark considering the integration of multiview and multimodal information for comprehensive 3D anomaly detection and segmentation (ADS), where the t…
Towards Reliable Identification of Diffusion-based Image Manipulations
Alex Costanzino, Woody Bayliss, Juil Sock +5
Changing facial expressions, gestures, or background details may dramatically alter the meaning conveyed by an image. Notably, recent advances in diffusion models greatly improve t…
NTIRE 2025 Challenge on HR Depth from Images of Specular and Transparent Surfaces
Pierluigi Zama Ramirez, Fabio Tosi, Luigi Di Stefano +36
This paper reports on the NTIRE 2025 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhanc…
Learning to Be a Transformer to Pinpoint Anomalies
Alex Costanzino, Pierluigi Zama Ramirez, Giuseppe Lisanti +1
To efficiently deploy strong, often pre-trained feature extractors, recent Industrial Anomaly Detection and Segmentation (IADS) methods process low-resolution images, e.g., 224x224…
Test Time Training for Industrial Anomaly Segmentation
Alex Costanzino, Pierluigi Zama Ramirez, Mirko Del Moro +4
Anomaly Detection and Segmentation (AD&S) is crucial for industrial quality control. While existing methods excel in generating anomaly scores for each pixel, practical application…
Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping
Alex Costanzino, Pierluigi Zama Ramirez, Giuseppe Lisanti +1
The paper explores the industrial multimodal Anomaly Detection (AD) task, which exploits point clouds and RGB images to localize anomalies. We introduce a novel light and fast fram…
Learning Depth Estimation for Transparent and Mirror Surfaces
Alex Costanzino, Pierluigi Zama Ramirez, Matteo Poggi +3
Inferring the depth of transparent or mirror (ToM) surfaces represents a hard challenge for either sensors, algorithms, or deep networks. We propose a simple pipeline for learning…
Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces
Pierluigi Zama Ramirez, Alex Costanzino, Fabio Tosi +4
Estimating depth from images nowadays yields outstanding results, both in terms of in-domain accuracy and generalization. However, we identify two main challenges that remain open…