Rémi Cadène
Rémi Cadène
Sorbonne University
Verified email at - Homepage
Cited by
Cited by
Benchmark analysis of representative deep neural network architectures
S Bianco, R Cadene, L Celona, P Napoletano
IEEE access 6, 64270-64277, 2018
Mutan: Multimodal tucker fusion for visual question answering
H Ben-Younes, R Cadene, M Cord, N Thome
Proceedings of the IEEE international conference on computer vision, 2612-2620, 2017
Murel: Multimodal relational reasoning for visual question answering
R Cadene, H Ben-Younes, M Cord, N Thome
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019
Rubi: Reducing unimodal biases for visual question answering
R Cadene, C Dancette, M Cord, D Parikh
Advances in neural information processing systems 32, 2019
Cross-modal retrieval in the cooking context: Learning semantic text-image embeddings
M Carvalho, R Cadène, D Picard, L Soulier, N Thome, M Cord
The 41st International ACM SIGIR Conference on Research & Development in …, 2018
Block: Bilinear superdiagonal fusion for visual question answering and visual relationship detection
H Ben-Younes, R Cadene, N Thome, M Cord
Proceedings of the AAAI conference on artificial intelligence 33 (01), 8102-8109, 2019
Beyond question-based biases: Assessing multimodal shortcut learning in visual question answering
C Dancette, R Cadene, D Teney, M Cord
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis
T Fel, R Cadène, M Chalvidal, M Cord, D Vigouroux, T Serre
Advances in Neural Information Processing Systems 34, 26005-26014, 2021
M2cai workflow challenge: convolutional neural networks with time smoothing and hidden Markov model for video frames classification
R Cadene, T Robert, N Thome, M Cord
arXiv preprint arXiv:1610.05541, 2016
What i cannot predict, i do not understand: A human-centered evaluation framework for explainability methods
J Colin, T Fel, R Cadène, T Serre
Advances in Neural Information Processing Systems 35, 2832-2845, 2022
How good is your explanation? algorithmic stability measures to assess the quality of explanations for deep neural networks
T Fel, D Vigouroux, R Cadène, T Serre
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022
Same-different conceptualization: a machine vision perspective
M Ricci, R Cadène, T Serre
Current Opinion in Behavioral Sciences 37, 47-55, 2021
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis
T Fel, M Ducoffe, D Vigouroux, R Cadène, M Capelle, C Nicodème, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
Understanding the computational demands underlying visual reasoning
M Vaishnav, R Cadene, A Alamia, D Linsley, R VanRullen, T Serre
Neural Computation 34 (5), 1075-1099, 2022
Master's Thesis: Deep Learning for Visual Recognition
R Cadène, N Thome, M Cord
arXiv preprint arXiv:1610.05567, 2016
Xplique: A deep learning explainability toolbox
T Fel, L Hervier, D Vigouroux, A Poche, J Plakoo, R Cadene, M Chalvidal, ...
arXiv preprint arXiv:2206.04394, 2022
Images and recipes: Retrieval in the cooking context
M Carvalho, R Cadene, D Picard, L Soulier, M Cord
2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW …, 2018
Craft: Concept recursive activation factorization for explainability
T Fel, A Picard, L Bethune, T Boissin, D Vigouroux, J Colin, R Cadène, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
Deep learning and image classification on a medium dataset of cooking recipes
R Cadene
Online: http://remicadene. com/uploads/summer-internship-2015. pdf, 2015
Deep Multimodal Learning for Vision and Language Processing
R Cadène
Sorbonne Université, UPMC, 2020
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