Portrait of David Vázquez

David Vázquez

Associate Industry Member
Adjunct Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineerin
ServiceNow
Research Topics
Computer Vision
Conversational AI
Deep Learning
Generative Models
Large Language Models (LLM)
Multimodal Learning
Representation Learning
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Publications

Multi-label Iterated Learning for Image Classification with Label Ambiguity
Sai Rajeswar
Pau Rodriguez
Soumye Singhal
David Vázquez
Aaron Courville
Transfer learning from large-scale pre-trained models has become essential for many computer vision tasks. Recent studies have shown that da… (see more)tasets like ImageNet are weakly labeled since images with multiple object classes present are assigned a single label. This ambiguity biases models towards a single prediction, which could result in the suppression of classes that tend to co-occur in the data. Inspired by language emergence literature, we propose multi-label iterated learning (MILe) to incorporate the inductive biases of multi-label learning from single labels using the framework of iterated learning. MILe is a simple yet effective procedure that builds a multi-label description of the image by propagating binary predictions through successive generations of teacher and student networks with a learning bottleneck. Experiments show that our approach exhibits systematic benefits on ImageNet accuracy as well as ReaL F1 score, which indicates that MILe deals better with label ambiguity than the standard training procedure, even when fine-tuning from self-supervised weights. We also show that MILe is effective reducing label noise, achieving state-of-the-art performance on real-world large-scale noisy data such as WebVision. Furthermore, MILe improves performance in class incremental settings such as IIRC and it is robust to distribution shifts. Code: https://github.com/rajeswar18/MILe
2021-11-23
ArXiv (preprint)
doi.org
arxiv.org
A Survey of Self-Supervised and Few-Shot Object Detection
Gabriel Huang
Issam Hadj Laradji
David Vázquez
Simon Lacoste-Julien
Pau Rodriguez
Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require … (see more)dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions. Project page: https://gabrielhuang.github.io/fsod-survey/.
2021-10-27
ArXiv (preprint)
doi.org
arxiv.org
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Pau Rodriguez
Massimo Caccia
Alexandre Lacoste
Lee Zamparo
Issam Hadj Laradji
Laurent Charlin
David Vázquez
Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying… (see more) more reliable machine-learning systems. In computer vision applications, generative counterfactual methods indicate how to perturb a model’s input to change its prediction, providing details about the model’s decision-making. Current methods tend to generate trivial counterfactuals about a model’s decisions, as they often suggest to exaggerate or remove the presence of the attribute being classified. For the machine learning practitioner, these types of counterfactuals offer little value, since they provide no new information about undesired model or data biases. In this work, we identify the problem of trivial counterfactual generation and we propose DiVE to alleviate it. DiVE learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss to uncover multiple valuable explanations about the model’s prediction. Further, we introduce a mechanism to prevent the model from producing trivial explanations. Experiments on CelebA and Synbols demonstrate that our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods. Code is available at https://github.com/ElementAI/beyond-trivial-explanations.
2021-10-10
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (published)
doi.org
arxiv.org
Haptics-based Curiosity for Sparse-reward Tasks
Sai Rajeswar
Cyril Ibrahim
Nitin Surya
Florian Golemo
David Vázquez
Aaron Courville
Pedro O. Pinheiro
Robots in many real-world settings have access to force/torque sensors in their gripper and tactile sensing is often necessary for tasks tha… (see more)t involve contact-rich motion. In this work, we leverage surprise from mismatches in haptics feedback to guide exploration in hard sparse-reward reinforcement learning tasks. Our approach, Haptics-based Curiosity (\method{}), learns what visible objects interactions are supposed to ``feel" like. We encourage exploration by rewarding interactions where the expectation and the experience do not match. We test our approach on a range of haptics-intensive robot arm tasks (e.g. pushing objects, opening doors), which we also release as part of this work. Across multiple experiments in a simulated setting, we demonstrate that our method is able to learn these difficult tasks through sparse reward and curiosity alone. We compare our cross-modal approach to single-modality (haptics- or vision-only) approaches as well as other curiosity-based methods and find that our method performs better and is more sample-efficient.
2021-09-13
robot-learning.org/CoRL/2021/Conference (poster)
proceedings.mlr.press
openreview.net
Sequoia: A Software Framework to Unify Continual Learning Research
Fabrice Normandin
Florian Golemo
Oleksiy Ostapenko
Pau Rodriguez
Matthew D Riemer
J. Hurtado
Lucas Cecchi
Khimya Khetarpal
Dominic Zhao
Ryan Lindeborg
Timothee LESORT
David Vázquez
Laurent Charlin
Irina Rish
Massimo Caccia
The field of Continual Learning (CL) seeks to develop algorithms that accumulate knowledge and skills over time through interaction with non… (see more)-stationary environments. In practice, a plethora of evaluation procedures (settings) and algorithmic solutions (methods) exist, each with their own potentially disjoint set of assumptions. This variety makes measuring progress in CL difficult. We propose a taxonomy of settings, where each setting is described as a set of assumptions. A tree-shaped hierarchy emerges from this view, where more general settings become the parents of those with more restrictive assumptions. This makes it possible to use inheritance to share and reuse research, as developing a method for a given setting also makes it directly applicable onto any of its children. We instantiate this idea as a publicly available software framework called Sequoia, which features a wide variety of settings from both the Continual Supervised Learning (CSL) and Continual Reinforcement Learning (CRL) domains. Sequoia also includes a growing suite of methods which are easy to extend and customize, in addition to more specialized methods from external libraries. We hope that this new paradigm and its first implementation can help unify and accelerate research in CL. You can help us grow the tree by visiting (this GitHub URL).
2021-08-02
ArXiv (preprint)
openreview.net
openreview.net
Touch-based Curiosity for Sparse-Reward Tasks
Sai Rajeswar
Cyril Ibrahim
Nitin Surya
Florian Golemo
David Vázquez
Aaron Courville
Pedro O. Pinheiro
2021-04-01
ArXiv (preprint)
arxiv.org
arxiv.org
Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images Using a View-Based Representation
Sai Rajeswar
Fahim Mannan
Florian Golemo
Jérôme Parent-lévesque
David Vázquez
Derek Nowrouzezahrai
Aaron Courville
2020-03-20
International Journal of Computer Vision (published)
doi.org
arxiv.org
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
Massimo Caccia
Pau Rodriguez
Oleksiy Ostapenko
Fabrice Normandin
Min Lin
Lucas Caccia
Issam Hadj Laradji
Irina Rish
Alexande Lacoste
David Vázquez
Laurent Charlin
2020-03-12
ArXiv (preprint)
arxiv.org
arxiv.org
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning.
Massimo Caccia
Pau Rodriguez
Oleksiy Ostapenko
Fabrice Normandin
Min Lin
Lucas Caccia
Issam Hadj Laradji
Irina Rish
Alexandre Lacoste
David Vázquez
Laurent Charlin
Synbols: Probing Learning Algorithms with Synthetic Datasets
Alexandre Lacoste
Pau Rodr'iguez
Frédéric Branchaud-charron
Parmida Atighehchian
Massimo Caccia
Issam Hadj Laradji
Alexandre Drouin
Matt P. Craddock
Laurent Charlin
David Vázquez
arxiv.org
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
David Vázquez
Jorge Bernal
F. Javier Sánchez
Gloria Fernández-Esparrach
Antonio M. López
Adriana Romero Soriano
Michal Drozdzal
Aaron Courville
Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to… (see more) perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss rate and the inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing decision support systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs). We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.
2017-07-26
Journal of Healthcare Engineering (published)
doi.org
arxiv.org
PixelVAE: A Latent Variable Model for Natural Images
Ishaan Gulrajani
Kundan Kumar
Faruk Ahmed
Adrien Ali Taiga
Francesco Visin
David Vázquez
Aaron Courville
Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representatio… (see more)n and model global structure well but have difficulty capturing small details. PixelCNN models details very well, but lacks a latent code and is difficult to scale for capturing large structures. We present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. Our model requires very few expensive autoregressive layers compared to PixelCNN and learns latent codes that are more compressed than a standard VAE while still capturing most non-trivial structure. Finally, we extend our model to a hierarchy of latent variables at different scales. Our model achieves state-of-the-art performance on binarized MNIST, competitive performance on 64 × 64 ImageNet, and high-quality samples on the LSUN bedrooms dataset.
2017-01-01
ICLR.cc/2017/conference (poster)
openreview.net
openreview.net