203 citations · 379 across the 11 of their papers we have counts for
10 papers · 1 filter
OCTET: Object-aware Counterfactual Explanations
Mehdi Zemni, Mickaël Chen, Éloi Zablocki +3
Nowadays, deep vision models are being widely deployed in safety-critical applications, e.g., autonomous driving, and explainability of such models is becoming a pressing concern.…
STEEX: Steering Counterfactual Explanations with Semantics
Paul Jacob, Éloi Zablocki, Hédi Ben-Younes +3
As deep learning models are increasingly used in safety-critical applications, explainability and trustworthiness become major concerns. For simple images, such as low-resolution f…
Raising context awareness in motion forecasting
Hédi Ben-Younes, Éloi Zablocki, Mickaël Chen +2
Learning-based trajectory prediction models have encountered great success, with the promise of leveraging contextual information in addition to motion history. Yet, we find that s…
Explainability of deep vision-based autonomous driving systems: Review and challenges
Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez +1
This survey reviews explainability methods for vision-based self-driving systems trained with behavior cloning. The concept of explainability has several facets and the need for ex…
RUBi: Reducing Unimodal Biases in Visual Question Answering
Remi Cadene, Corentin Dancette, Hedi Ben-younes +2
Visual Question Answering (VQA) is the task of answering questions about an image. Some VQA models often exploit unimodal biases to provide the correct answer without using the ima…
MUREL: Multimodal Relational Reasoning for Visual Question Answering
Remi Cadene, Hedi Ben-younes, Matthieu Cord +1
Multimodal attentional networks are currently state-of-the-art models for Visual Question Answering (VQA) tasks involving real images. Although attention allows to focus on the vis…
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection
Hedi Ben-younes, Rémi Cadene, Nicolas Thome +1
Multimodal representation learning is gaining more and more interest within the deep learning community. While bilinear models provide an interesting framework to find subtle combi…
OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation
Alexandre Rame, Emilien Garreau, Hedi Ben-Younes +1
Object detectors tend to perform poorly in new or open domains, and require exhaustive yet costly annotations from fully labeled datasets. We aim at benefiting from several dataset…
Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction
Charles Corbière, Hedi Ben-Younes, Alexandre Ramé +1
In this paper, we present a method to learn a visual representation adapted for e-commerce products. Based on weakly supervised learning, our model learns from noisy datasets crawl…
MUTAN: Multimodal Tucker Fusion for Visual Question Answering
Hedi Ben-younes, Rémi Cadene, Matthieu Cord +1
Bilinear models provide an appealing framework for mixing and merging information in Visual Question Answering (VQA) tasks. They help to learn high level associations between quest…