18 citations · 58 across the 14 of their papers we have counts for
15 papers
Faithful Grounded Visual Reasoning via Learned Proxy-Tokens
Tom Hodemon, Mohamed Chaouch, Aboubacar Tuo +1
Multimodal Large Language Models (MLLMs) have achieved remarkable success in Visual Question Answering (VQA), yet their "black-box" nature hinders deployment in critical domains. G…
Benchmarking Adversarial Robustness and Adversarial Training Strategies for Object Detection
Alexis Winter, Jean-Vincent Martini, Romaric Audigier +2
Object detection models are critical components of automated systems, such as autonomous vehicles and perception-based robots, but their sensitivity to adversarial attacks poses a…
MonoProb: Self-Supervised Monocular Depth Estimation with Interpretable Uncertainty
Rémi Marsal, Florian Chabot, Angelique Loesch +2
Self-supervised monocular depth estimation methods aim to be used in critical applications such as autonomous vehicles for environment analysis. To circumvent the potential imperfe…
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…