Search Results for author: Lorenzo Seidenari

Found 13 papers, 2 papers with code

Forecasting Future Instance Segmentation with Learned Optical Flow and Warping

no code implementations15 Nov 2022 Andrea Ciamarra, Federico Becattini, Lorenzo Seidenari, Alberto del Bimbo

For an autonomous vehicle it is essential to observe the ongoing dynamics of a scene and consequently predict imminent future scenarios to ensure safety to itself and others.

Instance Segmentation Optical Flow Estimation +1

Online Deep Clustering with Video Track Consistency

no code implementations7 Jun 2022 Alessandra Alfani, Federico Becattini, Lorenzo Seidenari, Alberto del Bimbo

Several unsupervised and self-supervised approaches have been developed in recent years to learn visual features from large-scale unlabeled datasets.

Deep Clustering

SMEMO: Social Memory for Trajectory Forecasting

no code implementations23 Mar 2022 Francesco Marchetti, Federico Becattini, Lorenzo Seidenari, Alberto del Bimbo

Effective modeling of human interactions is of utmost importance when forecasting behaviors such as future trajectories.

Trajectory Forecasting

Learning Group Activities from Skeletons without Individual Action Labels

1 code implementation14 May 2021 Fabio Zappardino, Tiberio Uricchio, Lorenzo Seidenari, Alberto del Bimbo

To understand human behavior we must not just recognize individual actions but model possibly complex group activity and interactions.

Group Activity Recognition

Explaining Autonomous Driving by Learning End-to-End Visual Attention

no code implementations5 Jun 2020 Luca Cultrera, Lorenzo Seidenari, Federico Becattini, Pietro Pala, Alberto del Bimbo

Current deep learning based autonomous driving approaches yield impressive results also leading to in-production deployment in certain controlled scenarios.

Autonomous Driving Imitation Learning

Text-to-Image Synthesis Based on Machine Generated Captions

no code implementations9 Oct 2019 Marco Menardi, Alex Falcon, Saida S. Mohamed, Lorenzo Seidenari, Giuseppe Serra, Alberto del Bimbo, Carlo Tasso

To address this issue, in this paper we propose an approach capable of generating images starting from a given text using conditional GANs trained on uncaptioned images dataset.

Image Captioning Image Generation

Semantic Road Layout Understanding by Generative Adversarial Inpainting

no code implementations29 May 2018 Lorenzo Berlincioni, Federico Becattini, Leonardo Galteri, Lorenzo Seidenari, Alberto del Bimbo

Autonomous driving is becoming a reality, yet vehicles still need to rely on complex sensor fusion to understand the scene they act in.

Autonomous Driving Semantic Segmentation

Deep Generative Adversarial Compression Artifact Removal

no code implementations ICCV 2017 Leonardo Galteri, Lorenzo Seidenari, Marco Bertini, Alberto del Bimbo

Moreover we show that our approach can be used as a pre-processing step for object detection in case images are degraded by compression to a point that state-of-the art detectors fail.

object-detection Object Detection +1

Segmentation Free Object Discovery in Video

no code implementations1 Sep 2016 Giovanni Cuffaro, Federico Becattini, Claudio Baecchi, Lorenzo Seidenari, Alberto del Bimbo

In this paper we present a simple yet effective approach to extend without supervision any object proposal from static images to videos.

Object Discovery

Automatic Image Annotation via Label Transfer in the Semantic Space

no code implementations16 May 2016 Tiberio Uricchio, Lamberto Ballan, Lorenzo Seidenari, Alberto del Bimbo

Automatic image annotation is among the fundamental problems in computer vision and pattern recognition, and it is becoming increasingly important in order to develop algorithms that are able to search and browse large-scale image collections.


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