1 code implementation • 7 Feb 2023 • Laura Calem, Hedi Ben-Younes, Patrick Pérez, Nicolas Thome
Predicting multiple trajectories for road users is important for automated driving systems: ego-vehicle motion planning indeed requires a clear view of the possible motions of the surrounding agents.
1 code implementation • 24 Jun 2019 • Remi Cadene, Corentin Dancette, Hedi Ben-Younes, Matthieu Cord, Devi Parikh
We propose RUBi, a new learning strategy to reduce biases in any VQA model.
Ranked #7 on Visual Question Answering (VQA) on VQA-CP
1 code implementation • CVPR 2019 • Remi Cadene, Hedi Ben-Younes, Matthieu Cord, Nicolas Thome
In this paper, we propose MuRel, a multimodal relational network which is learned end-to-end to reason over real images.
Ranked #1 on Visual Question Answering (VQA) on TDIUC
1 code implementation • 31 Jan 2019 • Hedi Ben-Younes, Rémi Cadene, Nicolas Thome, Matthieu Cord
We demonstrate the practical interest of our fusion model by using BLOCK for two challenging tasks: Visual Question Answering (VQA) and Visual Relationship Detection (VRD), where we design end-to-end learnable architectures for representing relevant interactions between modalities.
no code implementations • 6 Dec 2018 • Alexandre Rame, Emilien Garreau, Hedi Ben-Younes, Charles Ollion
Similarly to self-training methods, the predictions of these initial detectors mitigate the missing annotations on the complementary datasets.
no code implementations • 27 Sep 2017 • Charles Corbière, Hedi Ben-Younes, Alexandre Ramé, Charles Ollion
In this paper, we present a method to learn a visual representation adapted for e-commerce products.
6 code implementations • ICCV 2017 • Hedi Ben-Younes, Rémi Cadene, Matthieu Cord, Nicolas Thome
Bilinear models provide an appealing framework for mixing and merging information in Visual Question Answering (VQA) tasks.
Ranked #35 on Visual Question Answering (VQA) on VQA v2 test-std