Zobrazeno 1 - 10
of 113
pro vyhledávání: '"Zanca, Dario"'
Autor:
Zanca, Dario, Zugarini, Andrea, Dietz, Simon, Altstidl, Thomas R., Ndjeuha, Mark A. Turban, Schwinn, Leo, Eskofier, Bjoern
Understanding human attention is crucial for vision science and AI. While many models exist for free-viewing, less is known about task-driven image exploration. To address this, we introduce CapMIT1003, a dataset with captions and click-contingent im
Externí odkaz:
http://arxiv.org/abs/2408.09948
Autor:
Panda, Mahadev Prasad, Tiezzi, Matteo, Vilas, Martina, Roig, Gemma, Eskofier, Bjoern M., Zanca, Dario
We introduce Foveation-based Explanations (FovEx), a novel human-inspired visual explainability (XAI) method for Deep Neural Networks. Our method achieves state-of-the-art performance on both transformer (on 4 out of 5 metrics) and convolutional mode
Externí odkaz:
http://arxiv.org/abs/2408.02123
Mixed-type time series (MTTS) is a bimodal data type that is common in many domains, such as healthcare, finance, environmental monitoring, and social media. It consists of regularly sampled continuous time series and irregularly sampled categorical
Externí odkaz:
http://arxiv.org/abs/2406.15098
Autor:
Cartella, Giuseppe, Cornia, Marcella, Cuculo, Vittorio, D'Amelio, Alessandro, Zanca, Dario, Boccignone, Giuseppe, Cucchiara, Rita
Human attention modelling has proven, in recent years, to be particularly useful not only for understanding the cognitive processes underlying visual exploration, but also for providing support to artificial intelligence models that aim to solve prob
Externí odkaz:
http://arxiv.org/abs/2402.18673
As the field of deep learning steadily transitions from the realm of academic research to practical application, the significance of self-supervised pretraining methods has become increasingly prominent. These methods, particularly in the image domai
Externí odkaz:
http://arxiv.org/abs/2305.12384
Autor:
Zanca, Dario, Zugarini, Andrea, Dietz, Simon, Altstidl, Thomas R., Ndjeuha, Mark A. Turban, Schwinn, Leo, Eskofier, Bjoern
Understanding the mechanisms underlying human attention is a fundamental challenge for both vision science and artificial intelligence. While numerous computational models of free-viewing have been proposed, less is known about the mechanisms underly
Externí odkaz:
http://arxiv.org/abs/2305.12380
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence, 37(7), 2023, 8317-8324
Clustering is at the very core of machine learning, and its applications proliferate with the increasing availability of data. However, as datasets grow, comparing clusterings with an adjustment for chance becomes computationally difficult, preventin
Externí odkaz:
http://arxiv.org/abs/2305.03022
Existing models of human visual attention are generally unable to incorporate direct task guidance and therefore cannot model an intent or goal when exploring a scene. To integrate guidance of any downstream visual task into attention modeling, we pr
Externí odkaz:
http://arxiv.org/abs/2211.12100
Autor:
Altstidl, Thomas, Nguyen, An, Schwinn, Leo, Köferl, Franz, Mutschler, Christopher, Eskofier, Björn, Zanca, Dario
The widespread success of convolutional neural networks may largely be attributed to their intrinsic property of translation equivariance. However, convolutions are not equivariant to variations in scale and fail to generalize to objects of different
Externí odkaz:
http://arxiv.org/abs/2211.10288
Autor:
Loeffler, Christoffer, Fallah, Kion, Fenu, Stefano, Zanca, Dario, Eskofier, Bjoern, Rozell, Christopher John, Mutschler, Christopher
Publikováno v:
Transactions on Machine Learning Research 04/2023 https://openreview.net/forum?id=oq3tx5kinu
Humans innately measure distance between instances in an unlabeled dataset using an unknown similarity function. Distance metrics can only serve as proxy for similarity in information retrieval of similar instances. Learning a good similarity functio
Externí odkaz:
http://arxiv.org/abs/2207.12710