research
∙
06/09/2023
GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields
Neural Radiance Fields (NeRF) have shown impressive novel view synthesis...
share
research
∙
04/04/2023
OrienterNet: Visual Localization in 2D Public Maps with Neural Matching
Humans can orient themselves in their 3D environments using simple 2D ma...
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...
research
∙
12/02/2022
DiffRF: Rendering-Guided 3D Radiance Field Diffusion
We introduce DiffRF, a novel approach for 3D radiance field synthesis ba...
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...
research
∙
01/31/2022
Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation
Deep neural networks have enabled major progresses in semantic segmentat...
research
∙
11/01/2021
Dense Prediction with Attentive Feature Aggregation
Aggregating information from features across different layers is an esse...
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...
research
∙
03/25/2021
Inferring Latent Domains for Unsupervised Deep Domain Adaptation
Unsupervised Domain Adaptation (UDA) refers to the problem of learning a...
research
∙
01/03/2021
Weakly Supervised Multi-Object Tracking and Segmentation
We introduce the problem of weakly supervised Multi-Object Tracking and ...
research
∙
12/14/2020
Improving Panoptic Segmentation at All Scales
Crop-based training strategies decouple training resolution from GPU mem...
research
∙
12/10/2020
Demystifying Pseudo-LiDAR for Monocular 3D Object Detection
Pseudo-LiDAR-based methods for monocular 3D object detection have genera...
research
∙
08/04/2020
Shape Consistent 2D Keypoint Estimation under Domain Shift
Recent unsupervised domain adaptation methods based on deep architecture...
research
∙
04/20/2020
Boosting Deep Open World Recognition by Clustering
While convolutional neural networks have brought significant advances in...
research
∙
02/03/2020
Modeling the Background for Incremental Learning in Semantic Segmentation
Despite their effectiveness in a wide range of tasks, deep architectures...
research
∙
12/23/2019
The Five Elements of Flow
In this work we propose five concrete steps to improve the performance o...
research
∙
12/17/2019
Single-Stage Monocular 3D Object Detection with Virtual Cameras
While expensive LiDAR and stereo camera rigs have enabled the developmen...
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...
research
∙
05/03/2019
Seamless Scene Segmentation
In this work we introduce a novel, CNN-based architecture that can be tr...
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...
research
∙
03/17/2019
AdaGraph: Unifying Predictive and ContinuousDomain Adaptation through Graphs
The ability to categorize is a cornerstone of visual intelligence, and a...
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...
research
∙
11/25/2018
Is Data Clustering in Adversarial Settings Secure?
Clustering algorithms have been increasingly adopted in security applica...
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...
research
∙
05/30/2018
Robust Place Categorization with Deep Domain Generalization
Traditional place categorization approaches in robot vision assume that ...
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...
research
∙
05/03/2018
Boosting Domain Adaptation by Discovering Latent Domains
Current Domain Adaptation (DA) methods based on deep architectures assum...
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-...
research
∙
04/10/2017
Loss Max-Pooling for Semantic Image Segmentation
We introduce a novel loss max-pooling concept for handling imbalanced tr...
research
∙
02/25/2017
Learning Deep NBNN Representations for Robust Place Categorization
This paper presents an approach for semantic place categorization using ...
research
∙
02/21/2017
