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Jean-François Lalonde

Associate Academic Member
Full Professor, Université Laval, Department of Electrical and Computer Engineering
Research Topics
Computational Photography
Computer Vision
Generative Models
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Current Students

Christophe Bolduc
PhD - Université Laval
Github
Frédéric Fortier-Chouinard
PhD - Université Laval
Website
Github
Google Scholar
Justine Giroux
PhD - Université Laval
Website
Github
Google Scholar
Siddhant Katyan
PhD - Université Laval
Github
Google Scholar
Jonathan Roussel
Master's Research - Université Laval
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Zitian Zhang
PhD - Université Laval
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Publications

TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers
Fatemeh Nourilenjan Nokabadi
Yann Batiste Pequignot
Jean-François Lalonde
Christian Gagné
2025-05-27
Proceedings of the Conference on Robots and Vision (published)
doi.org
openreview.net
Adversarial Bounding Boxes Generation (ABBG) Attack against Visual Object Trackers
Fatemeh Nourilenjan Nokabadi
Jean-François Lalonde
Christian Gagné
Adversarial perturbations aim to deceive neural networks into predicting inaccurate results. For visual object trackers, adversarial attacks… (see more) have been developed to generate perturbations by manipulating the outputs. However, transformer trackers predict a specific bounding box instead of an object candidate list, which limits the applicability of many existing attack scenarios. To address this issue, we present a novel white-box approach to attack visual object trackers with transformer backbones using only one bounding box. From the tracker predicted bounding box, we generate a list of adversarial bounding boxes and compute the adversarial loss for those bounding boxes. Experimental results demonstrate that our simple yet effective attack outperforms existing attacks against several robust transformer trackers, including TransT-M, ROMTrack, and MixFormer, on popular benchmark tracking datasets such as GOT-10k, UAV123, and VOT2022STS.
2024-11-26
ArXiv (preprint)
arxiv.org
arxiv.org
Adversarial Bounding Boxes Generation (ABBG) Attack against Visual Object Trackers
Fatemeh Nourilenjan Nokabadi
Jean-François Lalonde
Christian Gagné
Adversarial perturbations aim to deceive neural networks into predicting inaccurate results. For visual object trackers, adversarial attacks… (see more) have been developed to generate perturbations by manipulating the outputs. However, transformer trackers predict a specific bounding box instead of an object candidate list, which limits the applicability of many existing attack scenarios. To address this issue, we present a novel white-box approach to attack visual object trackers with transformer backbones using only one bounding box. From the tracker predicted bounding box, we generate a list of adversarial bounding boxes and compute the adversarial loss for those bounding boxes. Experimental results demonstrate that our simple yet effective attack outperforms existing attacks against several robust transformer trackers, including TransT-M, ROMTrack, and MixFormer, on popular benchmark tracking datasets such as GOT-10k, UAV123, and VOT2022STS.
2024-11-26
ArXiv (preprint)
doi.org
arxiv.org
Adversarial Bounding Boxes Generation (ABBG) Attack against Visual Object Trackers
Fatemeh Nourilenjan Nokabadi
Jean-François Lalonde
Christian Gagné
Adversarial perturbations aim to deceive neural networks into predicting inaccurate results. For visual object trackers, adversarial attacks… (see more) have been developed to generate perturbations by manipulating the outputs. However, transformer trackers predict a specific bounding box instead of an object candidate list, which limits the applicability of many existing attack scenarios. To address this issue, we present a novel white-box approach to attack visual object trackers with transformer backbones using only one bounding box. From the tracker predicted bounding box, we generate a list of adversarial bounding boxes and compute the adversarial loss for those bounding boxes. Experimental results demonstrate that our simple yet effective attack outperforms existing attacks against several robust transformer trackers, including TransT-M, ROMTrack, and MixFormer, on popular benchmark tracking datasets such as GOT-10k, UAV123, and VOT2022STS.
2024-10-15
NeurIPS.cc/2024/Workshop/AdvML-Frontiers (published)
doi.org
openreview.net
TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers
Fatemeh Nourilenjan Nokabadi
Yann Batiste Pequignot
Jean-François Lalonde
Christian Gagné
Adversarial perturbations can deceive neural networks by adding small, imperceptible noise to the input. Recent object trackers with transfo… (see more)rmer backbones have shown strong performance on tracking datasets, but their adversarial robustness has not been thoroughly evaluated. While transformer trackers are resilient to black-box attacks, existing white-box adversarial attacks are not universally applicable against these new transformer trackers due to differences in backbone architecture. In this work, we introduce TrackPGD, a novel white-box attack that utilizes predicted object binary masks to target robust transformer trackers. Built upon the powerful segmentation attack SegPGD, our proposed TrackPGD effectively influences the decisions of transformer-based trackers. Our method addresses two primary challenges in adapting a segmentation attack for trackers: limited class numbers and extreme pixel class imbalance. TrackPGD uses the same number of iterations as other attack methods for tracker networks and produces competitive adversarial examples that mislead transformer and non-transformer trackers such as MixFormerM, OSTrackSTS, TransT-SEG, and RTS on datasets including VOT2022STS, DAVIS2016, UAV123, and GOT-10k.
2024-10-15
NeurIPS.cc/2024/Workshop/AdvML-Frontiers (published)
openreview.net
openreview.net
TrackPGD: A White-box Attack using Binary Masks against Robust Transformer Trackers
Fatemeh Nourilenjan Nokabadi
Yann Batiste Pequignot
Jean-François Lalonde
Christian Gagné
2024-07-04
ArXiv (preprint)
doi.org
arxiv.org
Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers
Fatemeh Nourilenjan Nokabadi
Jean-François Lalonde
Christian Gagné
2024-05-26
TMLR (accepted)
doi.org
openreview.net
Domain Agnostic Image-to-image Translation using Low-Resolution Conditioning
Mohamed Abderrahmen Abid
Arman Afrasiyabi
Ihsen Hedhli
Jean‐François Lalonde
Christian Gagné
2023-05-08
ArXiv (preprint)
doi.org
arxiv.org
Reducing touching eyes, nose and mouth ('T-zone') to reduce the spread of infectious disease: A prospective study of motivational, volitional and non-reflective predictors.
Mackenzie Wilson
Zachary M. van Allen
Jeremy M. Grimshaw
Jamie C. Brehaut
Audrey Durand
Jean‐François Lalonde
Douglas G. Manuel
Susan Michie
Robert West
Justin Presseau
BACKGROUND The route into the body for many pathogens is through the eyes, nose and mouth (i.e., the 'T-zone') via inhalation or fomite-base… (see more)d transfer during face touching. It is important to understand factors that are associated with touching the T-zone to inform preventive strategies. PURPOSE To identify theory-informed predictors of intention to reduce facial 'T-zone' touching and self-reported 'T-zone' touching. METHODS We conducted a nationally representative prospective questionnaire study of Canadians. Respondents were randomized to answer questions about touching their eyes, nose, or mouth with a questionnaire assessing 11 factors from an augmented Health Action Process Approach at baseline: intention, outcome expectancies, risk perception, individual severity, self-efficacy, action planning, coping planning, social support, automaticity, goal facilitation and stability of context. At 2-week follow-up, we assessed HAPA-based indicators of self-regulatory activities (awareness of standards, effort, self-monitoring) and self-reported behaviour (primary dependent variable). RESULTS Of 656 Canadian adults recruited, 569 responded to follow-up (87% response rate). Across all areas of the 'T-zone', outcome expectancy was the strongest predictor of intention to reduce facial 'T-zone' touching, while self-efficacy was a significant predictor for only the eyes and mouth. Automaticity was the strongest predictor of behaviour at the 2-week follow-up. No sociodemographic or psychological factors predicted behaviour, with the exception of self-efficacy, which negatively predicted eye touching. CONCLUSION Findings suggest that focusing on reflective processes may increase intention to reduce 'T-zone' touching, while reducing actual 'T-zone' touching may require strategies that address the automatic nature of this behaviour.
2023-03-30
British Journal of Health Psychology (published)
doi.org
Matching Feature Sets for Few-Shot Image Classification
Arman Afrasiyabi
Hugo Larochelle
Jean‐François Lalonde
Christian Gagné
In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classific… (see more)ation methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for each image. We argue that a set-based representation intrinsically builds a richer representation of images from the base classes, which can subsequently better transfer to the few-shot classes. To do so, we propose to adapt existing feature extractors to instead produce sets of feature vectors from images. Our approach, dubbed SetFeat, embeds shallow self-attention mechanisms inside existing encoder architectures. The attention modules are lightweight, and as such our method results in encoders that have approximately the same number of parameters as their original versions. During training and inference, a set-to-set matching metric is used to perform image classification. The effectiveness of our proposed architecture and metrics is demonstrated via thorough experiments on standard few-shot datasets-namely miniImageNet, tieredImageNet, and CUB-in both the 1- and 5-shot scenarios. In all cases but one, our method outperforms the state-of-the-art.
2022-06-18
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)
doi.org
arxiv.org
Matching Feature Sets for Few-Shot Image Classification
Arman Afrasiyabi
Hugo Larochelle
Jean‐François Lalonde
Christian Gagné
In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classific… (see more)ation methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for each image. We argue that a set-based representation intrinsically builds a richer representation of images from the base classes, which can subsequently better transfer to the few-shot classes. To do so, we propose to adapt existing feature extractors to instead produce sets of feature vectors from images. Our approach, dubbed SetFeat, embeds shallow self-attention mechanisms inside existing encoder architectures. The attention modules are lightweight, and as such our method results in encoders that have approximately the same number of parameters as their original versions. During training and inference, a set-to-set matching metric is used to perform image classification. The effectiveness of our proposed architecture and metrics is demonstrated via thorough experiments on standard few-shot datasets-namely miniImageNet, tieredImageNet, and CUB-in both the 1- and 5-shot scenarios. In all cases but one, our method outperforms the state-of-the-art.
2022-04-02
ArXiv (preprint)
doi.org
arxiv.org