43 citations · 162 across the 34 of their papers we have counts for
35 papers · 1 filter
Efficient and Robust Camera-independent Multiview 3D Geometric Reconstruction from Noisy Monocular Depth Estimation and Multiple Point Matching
Marius Leordeanu
We present an efficient and robust method for 3D geometric reconstruction that is based solely on the camera-independent linear relationships among a given set of points, which are…
A self-supervised cyclic neural-analytic approach for novel view synthesis and 3D reconstruction
Dragos Costea, Alina Marcu, Marius Leordeanu
Generating novel views from recorded videos is crucial for enabling autonomous UAV navigation. Recent advancements in neural rendering have facilitated the rapid development of met…
Maia: A Real-time Non-Verbal Chat for Human-AI Interaction
Dragos Costea, Alina Marcu, Cristina Lazar +1
Modeling face-to-face communication in computer vision, which focuses on recognizing and analyzing nonverbal cues and behaviors during interactions, serves as the foundation for ou…
Multi-Task Hypergraphs for Semi-supervised Learning using Earth Observations
Mihai Pirvu, Alina Marcu, Alexandra Dobrescu +2
There are many ways of interpreting the world and they are highly interdependent. We exploit such complex dependencies and introduce a powerful multi-task hypergraph, in which ever…
Self-supervised Hypergraphs for Learning Multiple World Interpretations
Alina Marcu, Mihai Pirvu, Dragos Costea +5
We present a method for learning multiple scene representations given a small labeled set, by exploiting the relationships between such representations in the form of a multi-task…
JEDI: Joint Expert Distillation in a Semi-Supervised Multi-Dataset Student-Teacher Scenario for Video Action Recognition
Lucian Bicsi, Bogdan Alexe, Radu Tudor Ionescu +1
We propose JEDI, a multi-dataset semi-supervised learning method, which efficiently combines knowledge from multiple experts, learned on different datasets, to train and improve th…
Self-supervised novel 2D view synthesis of large-scale scenes with efficient multi-scale voxel carving
Alexandra Budisteanu, Dragos Costea, Alina Marcu +1
The task of generating novel views of real scenes is increasingly important nowadays when AI models become able to create realistic new worlds. In many practical applications, it i…
Learning a Fast 3D Spectral Approach to Object Segmentation and Tracking over Space and Time
Elena Burceanu, Marius Leordeanu
We pose video object segmentation as spectral graph clustering in space and time, with one graph node for each pixel and edges forming local space-time neighborhoods. We claim that…
TEACHTEXT: CrossModal Generalized Distillation for Text-Video Retrieval
Ioana Croitoru, Simion-Vlad Bogolin, Marius Leordeanu +4
In recent years, considerable progress on the task of text-video retrieval has been achieved by leveraging large-scale pretraining on visual and audio datasets to construct powerfu…
Iterative Knowledge Exchange Between Deep Learning and Space-Time Spectral Clustering for Unsupervised Segmentation in Videos
Emanuela Haller, Adina Magda Florea, Marius Leordeanu
We propose a dual system for unsupervised object segmentation in video, which brings together two modules with complementary properties: a space-time graph that discovers objects i…
Semantics through Time: Semi-supervised Segmentation of Aerial Videos with Iterative Label Propagation
Alina Marcu, Vlad Licaret, Dragos Costea +1
Semantic segmentation is a crucial task for robot navigation and safety. However, current supervised methods require a large amount of pixelwise annotations to yield accurate resul…
Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus
Marius Leordeanu, Mihai Pirvu, Dragos Costea +3
We address the challenging problem of semi-supervised learning in the context of multiple visual interpretations of the world by finding consensus in a graph of neural networks. Ea…
Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks
Iulia Duta, Andrei Nicolicioiu, Marius Leordeanu
Graph Neural Networks are perfectly suited to capture latent interactions between various entities in the spatio-temporal domain (e.g. videos). However, when an explicit structure…
In Search of Life: Learning from Synthetic Data to Detect Vital Signs in Videos
Florin Condrea, Victor-Andrei Ivan, Marius Leordeanu
Automatically detecting vital signs in videos, such as the estimation of heart and respiration rates, is a challenging research problem in computer vision with important applicatio…
Towards Automatic Annotation for Semantic Segmentation in Drone Videos
Alina Marcu, Dragos Costea, Vlad Licaret +1
Semantic segmentation is a crucial task for robot navigation and safety. However, it requires huge amounts of pixelwise annotations to yield accurate results. While recent progress…
Learning Navigation by Visual Localization and Trajectory Prediction
Iulia Paraicu, Marius Leordeanu
When driving, people make decisions based on current traffic as well as their desired route. They have a mental map of known routes and are often able to navigate without needing d…
Spacetime Graph Optimization for Video Object Segmentation
Emanuela Haller, Adina Magda Florea, Marius Leordeanu
We address the challenging task of foreground object discovery and segmentation in video. We introduce an efficient solution, suitable for both unsupervised and supervised scenario…
A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time
Elena Burceanu, Marius Leordeanu
We formulate object segmentation in video as a graph partitioning problem in space and time, in which nodes are pixels and their relations form local neighborhoods. We claim that t…
Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints
Andretti Naiden, Vlad Paunescu, Gyeongmo Kim +2
We propose Shift R-CNN, a hybrid model for monocular 3D object detection, which combines deep learning with the power of geometry. We adapt a Faster R-CNN network for regressing in…
Recurrent Space-time Graph Neural Networks
Andrei Nicolicioiu, Iulia Duta, Marius Leordeanu
Learning in the space-time domain remains a very challenging problem in machine learning and computer vision. Current computational models for understanding spatio-temporal visual…
Unsupervised learning of foreground object detection
Ioana Croitoru, Simion-Vlad Bogolin, Marius Leordeanu
Unsupervised learning poses one of the most difficult challenges in computer vision today. The task has an immense practical value with many applications in artificial intelligence…
Mining for meaning: from vision to language through multiple networks consensus
Iulia Duta, Andrei Liviu Nicolicioiu, Simion-Vlad Bogolin +1
Describing visual data into natural language is a very challenging task, at the intersection of computer vision, natural language processing and machine learning. Language goes wel…
A Multi-Stage Multi-Task Neural Network for Aerial Scene Interpretation and Geolocalization
Alina Marcu, Dragos Costea, Emil Slusanschi +1
Semantic segmentation and vision-based geolocalization in aerial images are challenging tasks in computer vision. Due to the advent of deep convolutional nets and the availability…
Learning a Robust Society of Tracking Parts using Co-occurrence Constraints
Elena Burceanu, Marius Leordeanu
Object tracking is an essential problem in computer vision that has been researched for several decades. One of the main challenges in tracking is to adapt to object appearance cha…