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pro vyhledávání: '"D'AMATO, GIUSEPPE"'
We propose a novel two-stage semi-supervised learning approach for training downsampling-upsampling semantic segmentation architectures. The first stage does not use backpropagation. Rather, it exploits the bio-inspired Hebbian principle "fire togeth
Externí odkaz:
http://arxiv.org/abs/2412.03192
Autor:
Coccomini, Davide Alessandro, Caldelli, Roberto, Falchi, Fabrizio, Gennaro, Claudio, Amato, Giuseppe
Image manipulation is rapidly evolving, allowing the creation of credible content that can be used to bend reality. Although the results of deepfake detectors are promising, deepfakes can be made even more complicated to detect through adversarial at
Externí odkaz:
http://arxiv.org/abs/2410.04205
Autor:
Ciampi, Luca, Messina, Nicola, Pierucci, Matteo, Amato, Giuseppe, Avvenuti, Marco, Falchi, Fabrizio
Recently, object counting has shifted towards class-agnostic counting (CAC), which counts instances of arbitrary object classes never seen during model training. With advancements in robust vision-and-language foundation models, there is a growing in
Externí odkaz:
http://arxiv.org/abs/2409.15953
Autor:
Coccomini, Davide Alessandro, Caldelli, Roberto, Amato, Giuseppe, Falchi, Fabrizio, Gennaro, Claudio
Deepfake technology is rapidly advancing, posing significant challenges to the detection of manipulated media content. Parallel to that, some adversarial attack techniques have been developed to fool the deepfake detectors and make deepfakes even mor
Externí odkaz:
http://arxiv.org/abs/2407.02670
Autor:
Coccomini, Davide Alessandro, Caldelli, Roberto, Gennaro, Claudio, Fiameni, Giuseppe, Amato, Giuseppe, Falchi, Fabrizio
Deepfake detectors are typically trained on large sets of pristine and generated images, resulting in limited generalization capacity; they excel at identifying deepfakes created through methods encountered during training but struggle with those gen
Externí odkaz:
http://arxiv.org/abs/2403.13479
Recently emerged technologies based on Deep Learning (DL) achieved outstanding results on a variety of tasks in the field of Artificial Intelligence (AI). However, these encounter several challenges related to robustness to adversarial inputs, ecolog
Externí odkaz:
http://arxiv.org/abs/2307.16236
For a long time, biology and neuroscience fields have been a great source of inspiration for computer scientists, towards the development of Artificial Intelligence (AI) technologies. This survey aims at providing a comprehensive review of recent bio
Externí odkaz:
http://arxiv.org/abs/2307.16235