1 citations · 1 across the 1 of their papers we have counts for
6 papers
DATE: Detecting Anomalies in Text via Self-Supervision of Transformers
Andrei Manolache, Florin Brad, Elena Burceanu
Leveraging deep learning models for Anomaly Detection (AD) has seen widespread use in recent years due to superior performances over traditional methods. Recent deep methods for an…
Self-Supervised Learning in Multi-Task Graphs through Iterative Consensus Shift
Emanuela Haller, Elena Burceanu, Marius Leordeanu
The human ability to synchronize the feedback from all their senses inspired recent works in multi-task and multi-modal learning. While these works rely on expensive supervision, o…
SFTrack++: A Fast Learnable Spectral Segmentation Approach for Space-Time Consistent Tracking
Elena Burceanu
We propose an object tracking method, SFTrack++, that smoothly learns to preserve the tracked object consistency over space and time dimensions by taking a spectral clustering appr…
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…
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…
Learning a Robust Society of Tracking Parts
Elena Burceanu, Marius Leordeanu
Object tracking is an essential task in computer vision that has been studied since the early days of the field. Being able to follow objects that undergo different transformations…