no code implementations • 26 Mar 2023 • Ben Vardi, Alessandro Torcinovich, Marina Khoroshiltseva, Marcello Pelillo, Ohad Ben-Shahar
We present a novel method for solving square jigsaw puzzles based on global optimization.
no code implementations • 4 Apr 2022 • Ismail Elezi, Jenny Seidenschwarz, Laurin Wagner, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixe
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes.
1 code implementation • 9 Sep 2020 • Antonio Emanuele Cinà, Alessandro Torcinovich, Marcello Pelillo
In an attempt to fill this gap, in this work, we propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algorithms.
2 code implementations • ECCV 2020 • Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixe
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes.
Ranked #20 on Metric Learning on CUB-200-2011 (using extra training data)
no code implementations • 6 May 2019 • Sebastiano Vascon, Sinem Aslan, Alessandro Torcinovich, Twan van Laarhoven, Elena Marchiori, Marcello Pelillo
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain.
no code implementations • 26 May 2018 • Ismail Elezi, Alessandro Torcinovich, Sebastiano Vascon, Marcello Pelillo
Motivated by the observation that in virtually all application domains it is easy to obtain unlabeled data, in this paper we take a different perspective and propose a \emph{label augmentation} approach.
no code implementations • 21 Mar 2017 • Marco Fiorucci, Alessandro Torcinovich, Manuel Curado, Francisco Escolano, Marcello Pelillo
In this paper we analyze the practical implications of Szemer\'edi's regularity lemma in the preservation of metric information contained in large graphs.