1 code implementation • CVPR 2019 • Davide Abati, Angelo Porrello, Simone Calderara, Rita Cucchiara
Novelty detection is commonly referred to as the discrimination of observations that do not conform to a learned model of regularity.
no code implementations • 13 Feb 2019 • Luca Bergamini, Angelo Porrello, Andrea Capobianco Dondona, Ercole Del Negro, Mauro Mattioli, Nicola D'Alterio, Simone Calderara
People re-identification task has seen enormous improvements in the latest years, mainly due to the development of better image features extraction from deep Convolutional Neural Networks (CNN) and the availability of large datasets.
no code implementations • 13 Feb 2019 • Angelo Porrello, Davide Abati, Simone Calderara, Rita Cucchiara
We present a novel and hierarchical approach for supervised classification of signals spanning over a fixed graph, reflecting shared properties of the dataset.
no code implementations • 22 Nov 2019 • Stefano Vincenzi, Angelo Porrello, Pietro Buzzega, Annamaria Conte, Carla Ippoliti, Luca Candeloro, Alessio Di Lorenzo, Andrea Capobianco Dondona, Simone Calderara
Nowadays, Vector-Borne Diseases (VBDs) raise a severe threat for public health, accounting for a considerable amount of human illnesses.
3 code implementations • NeurIPS 2020 • Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, Simone Calderara
Continual Learning has inspired a plethora of approaches and evaluation settings; however, the majority of them overlooks the properties of a practical scenario, where the data stream cannot be shaped as a sequence of tasks and offline training is not viable.
Ranked #12 on Continual Learning on ASC (19 tasks)
1 code implementation • 22 Jun 2020 • Stefano Vincenzi, Angelo Porrello, Pietro Buzzega, Marco Cipriano, Pietro Fronte, Roberto Cuccu, Carla Ippoliti, Annamaria Conte, Simone Calderara
We conduct experiments on land cover classification (BigEarthNet) and West Nile Virus detection, showing that colorization is a solid pretext task for training a feature extractor.
1 code implementation • ECCV 2020 • Angelo Porrello, Luca Bergamini, Simone Calderara
To achieve robustness in Re-Identification, standard methods leverage tracking information in a Video-To-Video fashion.
Ranked #2 on Vehicle Re-Identification on VeRi
2 code implementations • 12 Oct 2020 • Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara
In Continual Learning, a Neural Network is trained on a stream of data whose distribution shifts over time.
1 code implementation • 14 Aug 2021 • Matteo Boschini, Pietro Buzzega, Lorenzo Bonicelli, Angelo Porrello, Simone Calderara
This work explores Continual Semi-Supervised Learning (CSSL): here, only a small fraction of labeled input examples are shown to the learner.
1 code implementation • 3 Jan 2022 • Matteo Boschini, Lorenzo Bonicelli, Pietro Buzzega, Angelo Porrello, Simone Calderara
The staple of human intelligence is the capability of acquiring knowledge in a continuous fashion.
no code implementations • CVPR 2022 • Alessio Monti, Angelo Porrello, Simone Calderara, Pasquale Coscia, Lamberto Ballan, Rita Cucchiara
To this end, we conceive a novel distillation strategy that allows a knowledge transfer from a teacher network to a student one, the latter fed with fewer observations (just two ones).
1 code implementation • 1 Jun 2022 • Matteo Boschini, Lorenzo Bonicelli, Angelo Porrello, Giovanni Bellitto, Matteo Pennisi, Simone Palazzo, Concetto Spampinato, Simone Calderara
This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL).
1 code implementation • 10 Aug 2022 • Aniello Panariello, Angelo Porrello, Simone Calderara, Rita Cucchiara
This work tackles Weakly Supervised Anomaly detection, in which a predictor is allowed to learn not only from normal examples but also from a few labeled anomalies made available during training.
Ranked #11 on Anomaly Detection In Surveillance Videos on XD-Violence
Anomaly Detection In Surveillance Videos Self-Supervised Learning +3
no code implementations • 7 Sep 2022 • Lorenzo Bonicelli, Angelo Porrello, Stefano Vincenzi, Carla Ippoliti, Federica Iapaolo, Annamaria Conte, Simone Calderara
In this paper, we seek to predict WNV circulation by feeding Deep Neural Networks (DNNs) with satellite images, which have been extensively shown to hold environmental and climatic features.
1 code implementation • 12 Oct 2022 • Lorenzo Bonicelli, Matteo Boschini, Angelo Porrello, Concetto Spampinato, Simone Calderara
By means of extensive experiments, we show that applying LiDER delivers a stable performance gain to several state-of-the-art rehearsal CL methods across multiple datasets, both in the presence and absence of pre-training.
1 code implementation • 27 Jan 2023 • Mang Ning, Enver Sangineto, Angelo Porrello, Simone Calderara, Rita Cucchiara
Denoising Diffusion Probabilistic Models have shown an impressive generation quality, although their long sampling chain leads to high computational costs.
Ranked #1 on Image Generation on FFHQ 128 x 128
no code implementations • 5 May 2023 • Lorenzo Bonicelli, Matteo Boschini, Emanuele Frascaroli, Angelo Porrello, Matteo Pennisi, Giovanni Bellitto, Simone Palazzo, Concetto Spampinato, Simone Calderara
Humans can learn incrementally, whereas neural networks forget previously acquired information catastrophically.
no code implementations • ICCV 2023 • Gianluca Mancusi, Aniello Panariello, Angelo Porrello, Matteo Fabbri, Simone Calderara, Rita Cucchiara
The field of multi-object tracking has recently seen a renewed interest in the good old schema of tracking-by-detection, as its simplicity and strong priors spare it from the complex design and painful babysitting of tracking-by-attention approaches.
no code implementations • 6 Jan 2024 • Aniello Panariello, Gianluca Mancusi, Fedy Haj Ali, Angelo Porrello, Simone Calderara, Rita Cucchiara
Existing approaches rely on two scales: local information (i. e., the bounding box proportions) or global information, which encodes the semantics of the scene as well as the spatial relations with neighboring objects.
1 code implementation • 22 Jan 2024 • Andrea Corsini, Angelo Porrello, Simone Calderara, Mauro Dell'Amico
Inspired by Semi- and Self-Supervised learning, we show that it is possible to easily train generative models by sampling multiple solutions and using the best one according to the problem objective as a pseudo-label.
no code implementations • 11 Mar 2024 • Martin Menabue, Emanuele Frascaroli, Matteo Boschini, Enver Sangineto, Lorenzo Bonicelli, Angelo Porrello, Simone Calderara
Most of these methods organize these vectors in a pool of key-value pairs, and use the input image as query to retrieve the prompts (values).
no code implementations • 29 Mar 2024 • Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi, Matteo Boschini, Angelo Porrello, Simone Calderara, Simone Palazzo, Concetto Spampinato
We present SAM, a biologically-plausible selective attention-driven modulation approach to enhance classification models in a continual learning setting.