1 citations · 2 across the 5 of their papers we have counts for
6 papers
WarNav: An Autonomous Driving Benchmark for Segmentation of Navigable Zones in War Scenes
Marc-Emmanuel Coupvent des Graviers, Hejer Ammar, Christophe Guettier +2
We introduce WarNav, a novel real-world dataset constructed from images of the open-source DATTALION repository, specifically tailored to enable the development and benchmarking of…
CaMiT: A Time-Aware Car Model Dataset for Classification and Generation
Frédéric LIN, Biruk Abere Ambaw, Adrian Popescu +3
AI systems must adapt to evolving visual environments, especially in domains where object appearances change over time. We introduce Car Models in Time (CaMiT), a fine-grained data…
xMOD: Cross-Modal Distillation for 2D/3D Multi-Object Discovery from 2D motion
Saad Lahlali, Sandra Kara, Hejer Ammar +4
Object discovery, which refers to the task of localizing objects without human annotations, has gained significant attention in 2D image analysis. However, despite this growing int…
Open-set object detection: towards unified problem formulation and benchmarking
Hejer Ammar, Nikita Kiselov, Guillaume Lapouge +1
In real-world applications where confidence is key, like autonomous driving, the accurate detection and appropriate handling of classes differing from those used during training ar…
The Background Also Matters: Background-Aware Motion-Guided Objects Discovery
Sandra Kara, Hejer Ammar, Florian Chabot +1
Recent works have shown that objects discovery can largely benefit from the inherent motion information in video data. However, these methods lack a proper background processing, r…
Image Segmentation-based Unsupervised Multiple Objects Discovery
Sandra Kara, Hejer Ammar, Florian Chabot +1
Unsupervised object discovery aims to localize objects in images, while removing the dependence on annotations required by most deep learning-based methods. To address this problem…