Search Results for author: Simone Calderara

Found 51 papers, 33 papers with code

Self-Labeling the Job Shop Scheduling Problem

1 code implementation22 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.

Job Shop Scheduling Pseudo Label +3

DistFormer: Enhancing Local and Global Features for Monocular Per-Object Distance Estimation

no code implementations6 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.

Autonomous Driving Object

TrackFlow: Multi-Object Tracking with Normalizing Flows

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.

Multi-Object Tracking Object

Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal

1 code implementation2 Feb 2023 Emanuele Marconato, Gianpaolo Bontempo, Elisa Ficarra, Simone Calderara, Andrea Passerini, Stefano Teso

We introduce Neuro-Symbolic Continual Learning, where a model has to solve a sequence of neuro-symbolic tasks, that is, it has to map sub-symbolic inputs to high-level concepts and compute predictions by reasoning consistently with prior knowledge.

Continual Learning

Input Perturbation Reduces Exposure Bias in Diffusion Models

1 code implementation27 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.

Denoising Image Generation +1

Latent Spectral Regularization for Continual Learning

1 code implementation9 Jan 2023 Emanuele Frascaroli, Riccardo Benaglia, Matteo Boschini, Luca Moschella, Cosimo Fiorini, Emanuele Rodolà, Simone Calderara

While biological intelligence grows organically as new knowledge is gathered throughout life, Artificial Neural Networks forget catastrophically whenever they face a changing training data distribution.

Continual Learning

On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning

1 code implementation12 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.

Continual Learning

Spotting Virus from Satellites: Modeling the Circulation of West Nile Virus Through Graph Neural Networks

no code implementations7 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.

Graph Attention

Consistency-based Self-supervised Learning for Temporal Anomaly Localization

1 code implementation10 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.

Anomaly Detection In Surveillance Videos Self-Supervised Learning +3

Learning the Quality of Machine Permutations in Job Shop Scheduling

no code implementations7 Jul 2022 Andrea Corsini, Simone Calderara, Mauro Dell'Amico

Finally, we empirically demonstrate the value of predicting the quality of machine permutations by enhancing the performance of a simple Tabu Search algorithm inspired by the works in the literature.

Combinatorial Optimization Job Shop Scheduling +1

Effects of Auxiliary Knowledge on Continual Learning

1 code implementation3 Jun 2022 Giovanni Bellitto, Matteo Pennisi, Simone Palazzo, Lorenzo Bonicelli, Matteo Boschini, Simone Calderara, Concetto Spampinato

In this paper we propose a new, simple, CL algorithm that focuses on solving the current task in a way that might facilitate the learning of the next ones.

Continual Learning Image Classification

SeeFar: Vehicle Speed Estimation and Flow Analysis from a Moving UAV

no code implementations ICIAP 2022 Mang Ning, Xiaoliang Ma, Yao Lu, Simone Calderara, Rita Cucchiara

In this paper, we introduce SeeFar to achieve vehicle speed estimation and traffic flow analysis based on YOLOv5 and DeepSORT from a moving drone.

Vehicle Speed Estimation

Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction

1 code implementation25 Apr 2022 Luigi Filippo Chiara, Pasquale Coscia, Sourav Das, Simone Calderara, Rita Cucchiara, Lamberto Ballan

Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and advanced video-surveillance applications.

Autonomous Vehicles Trajectory Forecasting

How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting

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).

Knowledge Distillation Trajectory Forecasting +1

Continual Semi-Supervised Learning through Contrastive Interpolation Consistency

1 code implementation14 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.

Continual Learning Metric Learning

Generalising via Meta-Examples for Continual Learning in the Wild

1 code implementation28 Jan 2021 Alessia Bertugli, Stefano Vincenzi, Simone Calderara, Andrea Passerini

Future deep learning systems call for techniques that can deal with the evolving nature of temporal data and scarcity of annotations when new problems occur.

Continual Learning Few-Shot Learning

Rethinking Experience Replay: a Bag of Tricks for Continual Learning

2 code implementations12 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.

Continual Learning

Few-Shot Unsupervised Continual Learning through Meta-Examples

1 code implementation17 Sep 2020 Alessia Bertugli, Stefano Vincenzi, Simone Calderara, Andrea Passerini

In real-world applications, data do not reflect the ones commonly used for neural networks training, since they are usually few, unlabeled and can be available as a stream.

Clustering Continual Learning +1

Inter-Homines: Distance-Based Risk Estimation for Human Safety

no code implementations20 Jul 2020 Matteo Fabbri, Fabio Lanzi, Riccardo Gasparini, Simone Calderara, Lorenzo Baraldi, Rita Cucchiara

In this document, we report our proposal for modeling the risk of possible contagiousity in a given area monitored by RGB cameras where people freely move and interact.

The color out of space: learning self-supervised representations for Earth Observation imagery

1 code implementation22 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.

Colorization Disease Prediction +4

DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting

1 code implementation26 May 2020 Alessio Monti, Alessia Bertugli, Simone Calderara, Rita Cucchiara

Understanding human motion behaviour is a critical task for several possible applications like self-driving cars or social robots, and in general for all those settings where an autonomous agent has to navigate inside a human-centric environment.

Human motion prediction Multi-future Trajectory Prediction +3

AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction

1 code implementation17 May 2020 Alessia Bertugli, Simone Calderara, Pasquale Coscia, Lamberto Ballan, Rita Cucchiara

Anticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications.

Graph Attention Multi-future Trajectory Prediction +2

Dark Experience for General Continual Learning: a Strong, Simple Baseline

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.

Class Incremental Learning Knowledge Distillation

Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation

1 code implementation CVPR 2020 Matteo Fabbri, Fabio Lanzi, Simone Calderara, Stefano Alletto, Rita Cucchiara

At the core of the proposed method lies our Volumetric Heatmap Autoencoder, a fully-convolutional network tasked with the compression of ground-truth heatmaps into a dense intermediate representation.

Ranked #6 on 3D Human Pose Estimation on Panoptic (using extra training data)

3D Human Pose Estimation 3D Pose Estimation

Conditional Channel Gated Networks for Task-Aware Continual Learning

1 code implementation CVPR 2020 Davide Abati, Jakub Tomczak, Tijmen Blankevoort, Simone Calderara, Rita Cucchiara, Babak Ehteshami Bejnordi

Therefore, we additionally introduce a task classifier that predicts the task label of each example, to deal with settings in which a task oracle is not available.

Continual Learning

Warp and Learn: Novel Views Generation for Vehicles and Other Objects

1 code implementation24 Jul 2019 Andrea Palazzi, Luca Bergamini, Simone Calderara, Rita Cucchiara

An Image Completion Network (ICN) is then trained to generate a realistic image starting from this geometric guidance.

3D Object Detection Image Generation

A Deep Learning based approach to VM behavior identification in cloud systems

1 code implementation5 Mar 2019 Matteo Stefanini, Riccardo Lancellotti, Lorenzo Baraldi, Simone Calderara

The experiments compare our proposal with state-of-the-art solutions available in literature, demonstrating that our proposal achieve better performance.

Cloud Computing Clustering +1

Multi-views Embedding for Cattle Re-identification

no code implementations13 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.

Classifying Signals on Irregular Domains via Convolutional Cluster Pooling

no code implementations13 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.

Clustering General Classification

Can Adversarial Networks Hallucinate Occluded People With a Plausible Aspect?

1 code implementation23 Jan 2019 Federico Fulgeri, Matteo Fabbri, Stefano Alletto, Simone Calderara, Rita Cucchiara

When you see a person in a crowd, occluded by other persons, you miss visual information that can be used to recognize, re-identify or simply classify him or her.


Latent Space Autoregression for Novelty Detection

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.

Anomaly Detection Novelty Detection +1

Face-from-Depth for Head Pose Estimation on Depth Images

no code implementations12 Dec 2017 Guido Borghi, Matteo Fabbri, Roberto Vezzani, Simone Calderara, Rita Cucchiara

Therefore, we propose a complete framework for the estimation of the head and shoulder pose based on depth images only.

Head Detection Head Pose Estimation +1

Generative Adversarial Models for People Attribute Recognition in Surveillance

no code implementations7 Jul 2017 Matteo Fabbri, Simone Calderara, Rita Cucchiara

In this paper we propose a deep architecture for detecting people attributes (e. g. gender, race, clothing ...) in surveillance contexts.

Attribute General Classification

Learning to Map Vehicles into Bird's Eye View

3 code implementations26 Jun 2017 Andrea Palazzi, Guido Borghi, Davide Abati, Simone Calderara, Rita Cucchiara

Awareness of the road scene is an essential component for both autonomous vehicles and Advances Driver Assistance Systems and is gaining importance both for the academia and car companies.

Autonomous Vehicles

Learning Where to Attend Like a Human Driver

1 code implementation24 Nov 2016 Andrea Palazzi, Francesco Solera, Simone Calderara, Stefano Alletto, Rita Cucchiara

Despite the advent of autonomous cars, it's likely - at least in the near future - that human attention will still maintain a central role as a guarantee in terms of legal responsibility during the driving task.

Learning to Divide and Conquer for Online Multi-Target Tracking

no code implementations ICCV 2015 Francesco Solera, Simone Calderara, Rita Cucchiara

Online Multiple Target Tracking (MTT) is often addressed within the tracking-by-detection paradigm.

Socially Constrained Structural Learning for Groups Detection in Crowd

no code implementations5 Aug 2015 Francesco Solera, Simone Calderara, Rita Cucchiara

Modern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of individuals.


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