6 papers
Flow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors
Orazio Pontorno, Mattia Litrico, Luca Guarnera +2
Current generative models, including GANs and diffusion models, have reached an outstanding level of photorealism, posing significant risks to privacy and security. To ensure real-…
Count2Density: Crowd Density Estimation without Location-level Annotations
Mattia Litrico, Feng Chen, Michael Pound +3
Crowd density estimation is a well-known computer vision task aimed at estimating the density distribution of people in an image. The main challenge in this domain is the reliance…
Temporally-Aware Diffusion Model for Brain Progression Modelling with Bidirectional Temporal Regularisation
Mattia Litrico, Francesco Guarnera, Mario Valerio Giuffrida +2
Generating realistic MRIs to accurately predict future changes in the structure of brain is an invaluable tool for clinicians in assessing clinical outcomes and analysing the disea…
TRUST: Leveraging Text Robustness for Unsupervised Domain Adaptation
Mattia Litrico, Mario Valerio Giuffrida, Sebastiano Battiato +1
Recent unsupervised domain adaptation (UDA) methods have shown great success in addressing classical domain shifts (e.g., synthetic-to-real), but they still suffer under complex sh…
PhenoAssistant: A Conversational Multi-Agent AI System for Automated Plant Phenotyping
Feng Chen, Ilias Stogiannidis, Andrew Wood +12
Plant phenotyping increasingly relies on (semi-)automated image-based analysis workflows to improve its accuracy and scalability. However, many existing solutions remain overly com…
GMT: Guided Mask Transformer for Leaf Instance Segmentation
Feng Chen, Sotirios A. Tsaftaris, Mario Valerio Giuffrida
Leaf instance segmentation is a challenging multi-instance segmentation task, aiming to separate and delineate each leaf in an image of a plant. Accurate segmentation of each leaf…