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Marcello Pelillo

5 accepted papers

2025

$\sigma$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples

ICLR 2025poster

Evaluating the adversarial robustness of deep networks to gradient-based attacks is challenging. While most attacks consider $\ell_2$- and $\ell_\infty$-norm constraints to craft input perturbations, only a few investigate sparse $\ell_1$- and $\ell_0$-norm attacks. In particular, $\ell_0$-norm atta…

Cited by 0SourcePDFScholar
2024

Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving

NeurIPS 2024poster

This paper proposes the RePAIR dataset that represents a challenging benchmark to test modern computational and data driven methods for puzzle-solving and reassembly tasks. Our dataset has unique properties that are uncommon to current benchmarks for 2D and 3D puzzle solving. The fragments and fract…

Cited by 3SourcePDFScholar
2020

The Group Loss for Deep Metric Learning

ECCV 2020poster

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. Much research has been devoted to the design of smart loss…

2018

DOTA: A Large-Scale Dataset for Object Detection in Aerial Images

CVPR 2018poster

Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge variation in the scale, orientation and shape of the…