papers

Publications (14)

cs.CV2022

PRIME: A few primitives can boost robustness to common corruptions

Apostolos Modas, Rahul Rade, Guillermo Ortiz-Jiménez +2

Despite their impressive performance on image classification tasks, deep networks have a hard time generalizing to unforeseen corruptions of their data. To fix this vulnerability,…

cs.LG2021

Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness

Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli +1

Driven by massive amounts of data and important advances in computational resources, new deep learning systems have achieved outstanding results in a large spectrum of applications…

cs.CV2023

Ethical Considerations for Responsible Data Curation

Jerone T. A. Andrews, Dora Zhao, William Thong +3

Human-centric computer vision (HCCV) data curation practices often neglect privacy and bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed th…

cs.CV2022

Robustness and invariance properties of image classifiers

Apostolos Modas

Deep neural networks have achieved impressive results in many image classification tasks. However, since their performance is usually measured in controlled settings, it is importa…

cs.LG2022

Data augmentation with mixtures of max-entropy transformations for filling-level classification

Apostolos Modas, Andrea Cavallaro, Pascal Frossard

We address the problem of distribution shifts in test-time data with a principled data augmentation scheme for the task of content-level classification. In such a task, properties…

cs.CV2020

Multi-view shape estimation of transparent containers

Alessio Xompero, Ricardo Sanchez-Matilla, Apostolos Modas +2

The 3D localisation of an object and the estimation of its properties, such as shape and dimensions, are challenging under varying degrees of transparency and lighting conditions.…