5 citations · 12 across the 12 of their papers we have counts for
13 papers
From Weights to Concepts: Data-Free Interpretability of CLIP via Singular Vector Decomposition
Francesco Gentile, Nicola Dall'Asen, Francesco Tonini +3
As vision-language models are deployed at scale, understanding their internal mechanisms becomes increasingly critical. Existing interpretability methods predominantly rely on acti…
MIL-PF: Multiple Instance Learning on Precomputed Features for Mammography Classification
Nikola Jovišić, Milica Škipina, Nicola Dall'Asen +1
Modern foundation models provide highly expressive visual representations, yet adapting them to high-resolution medical imaging remains challenging due to limited annotations and w…
Increasing the Utility of Synthetic Images through Chamfer Guidance
Nicola Dall'Asen, Xiaofeng Zhang, Reyhane Askari Hemmat +4
Conditional image generative models hold considerable promise to produce infinite amounts of synthetic training data. Yet, recent progress in generation quality has come at the exp…
MAMBO: High-Resolution Generative Approach for Mammography Images
Milica Škipina, Nikola Jovišić, Nicola Dall'Asen +5
Mammography is the gold standard for the detection and diagnosis of breast cancer. This procedure can be significantly enhanced with Artificial Intelligence (AI)-based software, wh…
Retrieval-enriched zero-shot image classification in low-resource domains
Nicola Dall'Asen, Yiming Wang, Enrico Fini +1
Low-resource domains, characterized by scarce data and annotations, present significant challenges for language and visual understanding tasks, with the latter much under-explored…
AL-GTD: Deep Active Learning for Gaze Target Detection
Francesco Tonini, Nicola Dall'Asen, Lorenzo Vaquero +2
Gaze target detection aims at determining the image location where a person is looking. While existing studies have made significant progress in this area by regressing accurate ga…
Adv-KD: Adversarial Knowledge Distillation for Faster Diffusion Sampling
Kidist Amde Mekonnen, Nicola Dall'Asen, Paolo Rota
Diffusion Probabilistic Models (DPMs) have emerged as a powerful class of deep generative models, achieving remarkable performance in image synthesis tasks. However, these models f…
Diversified in-domain synthesis with efficient fine-tuning for few-shot classification
Victor G. Turrisi da Costa, Nicola Dall'Asen, Yiming Wang +2
Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers…
Collaborative Neural Painting
Nicola Dall'Asen, Willi Menapace, Elia Peruzzo +3
The process of painting fosters creativity and rational planning. However, existing generative AI mostly focuses on producing visually pleasant artworks, without emphasizing the pa…
Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representations
Anil Osman Tur, Nicola Dall'Asen, Cigdem Beyan +1
This paper aims to address the unsupervised video anomaly detection (VAD) problem, which involves classifying each frame in a video as normal or abnormal, without any access to lab…
Object-aware Gaze Target Detection
Francesco Tonini, Nicola Dall'Asen, Cigdem Beyan +1
Gaze target detection aims to predict the image location where the person is looking and the probability that a gaze is out of the scene. Several works have tackled this task by re…
Exploring Diffusion Models for Unsupervised Video Anomaly Detection
Anil Osman Tur, Nicola Dall'Asen, Cigdem Beyan +1
This paper investigates the performance of diffusion models for video anomaly detection (VAD) within the most challenging but also the most operational scenario in which the data a…
Graph-based Generative Face Anonymisation with Pose Preservation
Nicola Dall'Asen, Yiming Wang, Hao Tang +2
We propose AnonyGAN, a GAN-based solution for face anonymisation which replaces the visual information corresponding to a source identity with a condition identity provided as any…