18 citations · 60 across the 13 of their papers we have counts for
14 papers
Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient ?
Quentin Bouniot, Angélique Loesch, Romaric Audigier +1
For specialized and dense downstream tasks such as object detection, labeling data requires expertise and can be very expensive, making few-shot and semi-supervised models much mor…
Proposal-Contrastive Pretraining for Object Detection from Fewer Data
Quentin Bouniot, Romaric Audigier, Angélique Loesch +1
The use of pretrained deep neural networks represents an attractive way to achieve strong results with few data available. When specialized in dense problems such as object detecti…
Spatio-temporal predictive tasks for abnormal event detection in videos
Yassine Naji, Aleksandr Setkov, Angélique Loesch +2
Abnormal event detection in videos is a challenging problem, partly due to the multiplicity of abnormal patterns and the lack of their corresponding annotations. In this paper, we…
Object-centric and memory-guided normality reconstruction for video anomaly detection
Khalil Bergaoui, Yassine Naji, Aleksandr Setkov +3
This paper addresses video anomaly detection problem for videosurveillance. Due to the inherent rarity and heterogeneity of abnormal events, the problem is viewed as a normality mo…
End-to-end Person Search Sequentially Trained on Aggregated Dataset
Angelique Loesch, Jaonary Rabarisoa, Romaric Audigier
In video surveillance applications, person search is a challenging task consisting in detecting people and extracting features from their silhouette for re-identification (re-ID) p…
Describe me if you can! Characterized Instance-level Human Parsing
Angelique Loesch, Romaric Audigier
Several computer vision applications such as person search or online fashion rely on human description. The use of instance-level human parsing (HP) is therefore relevant since it…