research
∙
08/21/2023
Multi-Task Hypergraphs for Semi-supervised Learning using Earth Observations
There are many ways of interpreting the world and they are highly interd...
share
research
∙
08/15/2023
Self-supervised Hypergraphs for Learning Multiple World Interpretations
We present a method for learning multiple scene representations given a ...
research
∙
08/09/2023
JEDI: Joint Expert Distillation in a Semi-Supervised Multi-Dataset Student-Teacher Scenario for Video Action Recognition
We propose JEDI, a multi-dataset semi-supervised learning method, which ...
research
∙
06/26/2023
Self-supervised novel 2D view synthesis of large-scale scenes with efficient multi-scale voxel carving
The task of generating novel views of real scenes is increasingly import...
research
∙
05/22/2023
GEST: the Graph of Events in Space and Time as a Common Representation between Vision and Language
One of the essential human skills is the ability to seamlessly build an ...
research
∙
03/30/2023
Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms
Pulmonary Embolisms (PE) represent a leading cause of cardiovascular dea...
research
∙
12/15/2022
Learning a Fast 3D Spectral Approach to Object Segmentation and Tracking over Space and Time
We pose video object segmentation as spectral graph clustering in space ...
research
∙
03/26/2021
Unsupervised Domain Adaptation through Iterative Consensus Shift in a Multi-Task Graph
Babies learn with very little supervision by observing the surrounding w...
research
∙
12/13/2020
Iterative Knowledge Exchange Between Deep Learning and Space-Time Spectral Clustering for Unsupervised Segmentation in Videos
We propose a dual system for unsupervised object segmentation in video, ...
research
∙
10/02/2020
Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus
We address the challenging problem of semi-supervised learning in the co...
research
∙
10/02/2020
Semantics through Time: Semi-supervised Segmentation of Aerial Videos with Iterative Label Propagation
Semantic segmentation is a crucial task for robot navigation and safety....
research
∙
06/23/2020
A self-supervised neural-analytic method to predict the evolution of COVID-19 in Romania
Analysing and understanding the transmission and evolution of the COVID-...
research
∙
04/16/2020
In Search of Life: Learning from Synthetic Data to Detect Vital Signs in Videos
Automatically detecting vital signs in videos, such as the estimation of...
research
∙
10/22/2019
Towards Automatic Annotation for Semantic Segmentation in Drone Videos
Semantic segmentation is a crucial task for robot navigation and safety....
research
∙
10/20/2019
Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)
Despite the significant advances in recent years, Generative Adversarial...
research
∙
10/07/2019
Learning Navigation by Visual Localization and Trajectory Prediction
When driving, people make decisions based on current traffic as well as ...
research
∙
07/07/2019
Spacetime Graph Optimization for Video Object Segmentation
In this paper we address the challenging task of object discovery and se...
research
∙
07/05/2019
A Spectral Approach to Unsupervised Object Segmentation in Video
We formulate object segmentation in video as a graph partitioning proble...
research
∙
05/23/2019
Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints
We propose Shift R-CNN, a hybrid model for monocular 3D object detection...
research
∙
04/11/2019
Recurrent Space-time Graphs for Video Understanding
Visual learning in the space-time domain remains a very challenging prob...
research
∙
08/14/2018
Unsupervised learning of foreground object detection
Unsupervised learning poses one of the most difficult challenges in comp...
research
∙
06/05/2018
Mining for meaning: from vision to language through multiple networks consensus
Describing visual data into natural language is a very challenging task,...
research
∙
04/05/2018
Learning a Robust Society of Tracking Parts using Co-occurrence Constraints
Object tracking is an essential problem in computer vision that has been...
research
∙
04/04/2018
A Multi-Stage Multi-Task Neural Network for Aerial Scene Interpretation and Geolocalization
Semantic segmentation and vision-based geolocalization in aerial images ...
research
∙
05/26/2017
Learning a Robust Society of Tracking Parts
Object tracking is an essential task in computer vision that has been st...
research
∙
05/23/2017
How hard can it be? Estimating the difficulty of visual search in an image
We address the problem of estimating image difficulty defined as the hum...
research
∙
03/31/2017
Unsupervised learning from video to detect foreground objects in single images
Unsupervised learning from visual data is one of the most difficult chal...
research
∙
05/26/2016
Aerial image geolocalization from recognition and matching of roads and intersections
Aerial image analysis at a semantic level is important in many applicati...
research
∙
12/01/2015
Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision
Feature selection is essential for effective visual recognition. We prop...
research
∙
11/20/2015
Stories in the Eye: Contextual Visual Interactions for Efficient Video to Language Translation
Integrating higher level visual and linguistic interpretations is at the...
research
∙
11/27/2014
Features in Concert: Discriminative Feature Selection meets Unsupervised Clustering
Feature selection is an essential problem in computer vision, important ...
research
∙
04/02/2014