M
Mariana-Iuliana Georgescu
Researcher at University of Bucharest
Publications - 26
Citations - 905
Mariana-Iuliana Georgescu is an academic researcher from University of Bucharest. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 6, co-authored 20 publications receiving 348 citations.
Papers
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Journal ArticleDOI
Local Learning With Deep and Handcrafted Features for Facial Expression Recognition
TL;DR: Zhang et al. as discussed by the authors proposed an approach that combines automatic features learned by convolutional neural networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) model in order to achieve the state of the art results in facial expression recognition (FER).
Proceedings ArticleDOI
Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video
TL;DR: An unsupervised feature learning framework based on object-centric convolutional auto-encoders to encode both motion and appearance information is introduced and a supervised classification approach based on clustering the training samples into normality clusters is proposed.
Posted Content
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video
TL;DR: Zhang et al. as mentioned in this paper proposed an unsupervised feature learning framework based on object-centric convolutional auto-encoders to encode both motion and appearance information.
Posted Content
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
Mariana-Iuliana Georgescu,Antonio Barbalau,Radu Tudor Ionescu,Fahad Shahbaz Khan,Marius Popescu,Mubarak Shah +5 more
TL;DR: This paper is the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture and outperforms the state-of-the-art methods on three benchmarks.
Proceedings ArticleDOI
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
Mariana-Iuliana Georgescu,Antonio Barbalau,Radu Tudor Ionescu,Fahad Shahbaz Khan,Marius Popescu,Mubarak Shah +5 more
TL;DR: In this article, a 3D convolutional neural network is trained to produce discriminative anomaly-specific information by jointly learning multiple proxy tasks: three self-supervised and one based on knowledge distillation.