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SICAGE: Speaker-Independent Culture-Aware Gesture Generation using TED4C-L Dataset
Ariel Gjaci, Antonio Sgorbissa, Vittorio Murino
Recent co-speech gesture generation methods often overlook cultural differences, limiting their effectiveness in human-agent interaction. Moreover, culture-conditioned models are r…
APEX: Assumption-free Projection-based Embedding eXamination Metric for Generative Distribution Evaluation
Caterina Gallegati, Monica Bianchini, Franco Scarselli +2
As generative models achieve unprecedented visual quality, the gold standard for image evaluation remains traditional feature-distribution metrics. However, these metrics are prova…
HAC: Parameter-Efficient Hyperbolic Adaptation of CLIP for Zero-Shot VQA
Francesco Dibitonto, Cigdem Beyan, Vittorio Murino
Recent advances in representation learning have shown that hyperbolic geometry can offer a more expressive alternative to the Euclidean embeddings used in CLIP models, capturing hi…
Discriminator-Guided Adaptive Diffusion for Source-Free Test-Time Adaptation under Image Corruptions
Francesco Olivato, Cigdem Beyan, Vittorio Murino
In this work, we study Source-Free Unsupervised Domain Adaptation under corruption-induced domain shifts, where performance degradation is caused by natural image corruptions that…
Lifelong Imitation Learning with Multimodal Latent Replay and Incremental Adjustment
Fanqi Yu, Matteo Tiezzi, Tommaso Apicella +2
We introduce a lifelong imitation learning framework that enables continual policy refinement across sequential tasks under realistic memory and data constraints. Our approach depa…
MADPOT: Medical Anomaly Detection with CLIP Adaptation and Partial Optimal Transport
Mahshid Shiri, Cigdem Beyan, Vittorio Murino
Medical anomaly detection (AD) is challenging due to diverse imaging modalities, anatomical variations, and limited labeled data. We propose a novel approach combining visual adapt…
MadCLIP: Few-shot Medical Anomaly Detection with CLIP
Mahshid Shiri, Cigdem Beyan, Vittorio Murino
An innovative few-shot anomaly detection approach is presented, leveraging the pre-trained CLIP model for medical data, and adapting it for both image-level anomaly classification…
A Mutual Information Perspective on Multiple Latent Variable Generative Models for Positive View Generation
Dario Serez, Marco Cristani, Alessio Del Bue +2
In image generation, Multiple Latent Variable Generative Models (MLVGMs) employ multiple latent variables to gradually shape the final images, from global characteristics to finer…
Pre-trained Multiple Latent Variable Generative Models are good defenders against Adversarial Attacks
Dario Serez, Marco Cristani, Alessio Del Bue +2
Attackers can deliberately perturb classifiers' input with subtle noise, altering final predictions. Among proposed countermeasures, adversarial purification employs generative net…
Upper-Body Pose-based Gaze Estimation for Privacy-Preserving 3D Gaze Target Detection
Andrea Toaiari, Vittorio Murino, Marco Cristani +1
Gaze Target Detection (GTD), i.e., determining where a person is looking within a scene from an external viewpoint, is a challenging task, particularly in 3D space. Existing approa…