Samuel Rota Bulò

research

∙ 12/19/2022

Panoptic Lifting for 3D Scene Understanding with Neural Fields

We propose Panoptic Lifting, a novel approach for learning panoptic 3D v...

0 Yawar Siddiqui, et al. ∙

research

∙ 12/02/2022

DiffRF: Rendering-Guided 3D Radiance Field Diffusion

We introduce DiffRF, a novel approach for 3D radiance field synthesis ba...

0 Norman Müller, et al. ∙

research

∙ 04/07/2022

AutoRF: Learning 3D Object Radiance Fields from Single View Observations

We introduce AutoRF - a new approach for learning neural 3D object repre...

0 Norman Müller, et al. ∙

research

∙ 01/31/2022

Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation

Deep neural networks have enabled major progresses in semantic segmentat...

14 Fabio Cermelli, et al. ∙

research

∙ 11/01/2021

Dense Prediction with Attentive Feature Aggregation

Aggregating information from features across different layers is an esse...

0 Yung-Hsu Yang, et al. ∙

research

∙ 03/25/2021

Boosting Binary Masks for Multi-Domain Learning through Affine Transformations

In this work, we present a new, algorithm for multi-domain learning. Giv...

0 Massimiliano Mancini, et al. ∙

research

∙ 03/25/2021

Inferring Latent Domains for Unsupervised Deep Domain Adaptation

Unsupervised Domain Adaptation (UDA) refers to the problem of learning a...

0 Massimiliano Mancini, et al. ∙

research

∙ 01/03/2021

Weakly Supervised Multi-Object Tracking and Segmentation

We introduce the problem of weakly supervised Multi-Object Tracking and ...

7 Idoia Ruiz, et al. ∙

research

∙ 12/14/2020

Improving Panoptic Segmentation at All Scales

Crop-based training strategies decouple training resolution from GPU mem...

0 Lorenzo Porzi, et al. ∙

research

∙ 12/10/2020

Demystifying Pseudo-LiDAR for Monocular 3D Object Detection

Pseudo-LiDAR-based methods for monocular 3D object detection have genera...

0 Andrea Simonelli, et al. ∙

research

∙ 08/04/2020

Shape Consistent 2D Keypoint Estimation under Domain Shift

Recent unsupervised domain adaptation methods based on deep architecture...

0 Levi O. Vasconcelos, et al. ∙

research

∙ 04/20/2020

Boosting Deep Open World Recognition by Clustering

While convolutional neural networks have brought significant advances in...

0 Dario Fontanel, et al. ∙

research

∙ 02/03/2020

Modeling the Background for Incremental Learning in Semantic Segmentation

Despite their effectiveness in a wide range of tasks, deep architectures...

0 Fabio Cermelli, et al. ∙

research

∙ 12/23/2019

The Five Elements of Flow

In this work we propose five concrete steps to improve the performance o...

22 Markus Hofinger, et al. ∙

research

∙ 12/17/2019

Single-Stage Monocular 3D Object Detection with Virtual Cameras

While expensive LiDAR and stereo camera rigs have enabled the developmen...

16 Andrea Simonelli, et al. ∙

research

∙ 12/04/2019

Learning Multi-Object Tracking and Segmentation from Automatic Annotations

In this work we contribute a novel pipeline to automatically generate tr...

0 Lorenzo Porzi, et al. ∙

research

∙ 05/03/2019

Seamless Scene Segmentation

In this work we introduce a novel, CNN-based architecture that can be tr...

0 Lorenzo Porzi, et al. ∙

research

∙ 03/17/2019

AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs

The ability to categorize is a cornerstone of visual intelligence, and a...

0 Massimiliano Mancini, et al. ∙

research

∙ 03/17/2019

AdaGraph: Unifying Predictive and ContinuousDomain Adaptation through Graphs

The ability to categorize is a cornerstone of visual intelligence, and a...

0 Massimiliano Mancini, et al. ∙

research

∙ 03/07/2019

Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss

A classifier trained on a dataset seldom works on other datasets obtaine...

0 Subhankar Roy, et al. ∙

research

∙ 11/25/2018

Is Data Clustering in Adversarial Settings Secure?

Clustering algorithms have been increasingly adopted in security applica...

0 Battista Biggio, et al. ∙

research

∙ 06/15/2018

Best sources forward: domain generalization through source-specific nets

A long standing problem in visual object categorization is the ability o...

0 Massimiliano Mancini, et al. ∙

research

∙ 05/30/2018

Robust Place Categorization with Deep Domain Generalization

Traditional place categorization approaches in robot vision assume that ...

0 Massimiliano Mancini, et al. ∙

research

∙ 05/28/2018

Adding New Tasks to a Single Network with Weight Trasformations using Binary Masks

Visual recognition algorithms are required today to exhibit adaptive abi...

0 Massimiliano Mancini, et al. ∙

research

∙ 05/03/2018

Boosting Domain Adaptation by Discovering Latent Domains

Current Domain Adaptation (DA) methods based on deep architectures assum...

0 Massimiliano Mancini, et al. ∙

research

∙ 12/07/2017

In-Place Activated BatchNorm for Memory-Optimized Training of DNNs

In this work we present In-Place Activated Batch Normalization (InPlace-...

0 Samuel Rota Bulò, et al. ∙

research

∙ 04/10/2017

Loss Max-Pooling for Semantic Image Segmentation

We introduce a novel loss max-pooling concept for handling imbalanced tr...

0 Samuel Rota Bulò, et al. ∙

research

∙ 02/25/2017

Learning Deep NBNN Representations for Robust Place Categorization

This paper presents an approach for semantic place categorization using ...

0 Massimiliano Mancini, et al. ∙

research

∙ 02/21/2017

Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation

The empirical fact that classifiers, trained on given data collections, ...

0 Fabio Maria Carlucci, et al. ∙

Samuel Rota Bulò

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