15 citations · 20 across the 8 of their papers we have counts for
9 papers
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…
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…
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…
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…
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…
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…
Unsupervised object segmentation in video by efficient selection of highly probable positive features
Emanuela Haller, Marius Leordeanu
We address an essential problem in computer vision, that of unsupervised object segmentation in video, where a main object of interest in a video sequence should be automatically s…