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Emanuela Haller

2 accepted papers

2022

AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection

NeurIPS 2022accept

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 scenario for studying the generalization properties of ML models. The existing benchmarks are focused on supervised learni…

2017

Unsupervised Object Segmentation in Video by Efficient Selection of Highly Probable Positive Features

ICCV 2017poster

We address an essential problem in computer vision, that of unsupervised foreground object segmentation in video, where a main object of interest in a video sequence should be automatically separated from its background. An efficient solution to this task would enable large-scale video interpretatio…

Cited by 38PDFScholar