15 citations · 24 across the 13 of their papers we have counts for
15 papers
ChronoGraph: A Real-World Graph-Based Multivariate Time Series Dataset
Adrian Catalin Lutu, Ioana Pintilie, Elena Burceanu +1
We present ChronoGraph, a graph-structured multivariate time series forecasting dataset built from real-world production microservices. Each node is a service that emits a multivar…
Inside Knowledge: Graph-based Path Generation with Explainable Data Augmentation and Curriculum Learning for Visual Indoor Navigation
Daniel Airinei, Elena Burceanu, Marius Leordeanu
Indoor navigation is a difficult task, as it generally comes with poor GPS access, forcing solutions to rely on other sources of information. While significant progress continues t…
Bridging Explainability and Embeddings: BEE Aware of Spuriousness
Cristian Daniel Păduraru, Antonio Bărbălau, Radu Filipescu +2
Current methods for detecting spurious correlations rely on analyzing dataset statistics or error patterns, leaving many harmful shortcuts invisible when counterexamples are absent…
Environment-biased Feature Ranking for Novelty Detection Robustness
Stefan Smeu, Elena Burceanu, Emanuela Haller +1
We tackle the problem of robust novelty detection, where we aim to detect novelties in terms of semantic content while being invariant to changes in other, irrelevant factors. Spec…
Robust Novelty Detection through Style-Conscious Feature Ranking
Stefan Smeu, Elena Burceanu, Emanuela Haller +1
Novelty detection seeks to identify samples deviating from a known distribution, yet data shifts in a multitude of ways, and only a few consist of relevant changes. Aligned with ou…
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…
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!
Stefan Smeu, Elena Burceanu, Andrei Liviu Nicolicioiu +1
We introduce a formalization and benchmark for the unsupervised anomaly detection task in the distribution-shift scenario. Our work builds upon the iWildCam dataset, and, to the be…
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection
Marius Dragoi, Elena Burceanu, Emanuela Haller +2
Analyzing the distribution shift of data is a growing research direction in nowadays Machine Learning (ML), leading to emerging new benchmarks that focus on providing a suitable sc…
Rethinking the Authorship Verification Experimental Setups
Florin Brad, Andrei Manolache, Elena Burceanu +3
One of the main drivers of the recent advances in authorship verification is the PAN large-scale authorship dataset. Despite generating significant progress in the field, inconsist…
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…