527 citations · 877 across the 56 of their papers we have counts for
16 papers · 1 filter
Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies
Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +4
Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. Moreover, t…
Deep Active Learning for Joint Classification & Segmentation with Weak Annotator
Soufiane Belharbi, Ismail Ben Ayed, Luke McCaffrey +1
CNN visualization and interpretation methods, like class-activation maps (CAMs), are typically used to highlight the image regions linked to class predictions. These models allow t…
Temporal Stochastic Softmax for 3D CNNs: An Application in Facial Expression Recognition
Théo Ayral, Marco Pedersoli, Simon Bacon +1
Training deep learning models for accurate spatiotemporal recognition of facial expressions in videos requires significant computational resources. For practical reasons, 3D Convol…
Augmented Lagrangian Adversarial Attacks
Jérôme Rony, Eric Granger, Marco Pedersoli +1
Adversarial attack algorithms are dominated by penalty methods, which are slow in practice, or more efficient distance-customized methods, which are heavily tailored to the propert…
Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks
Thomas Teixeira, Eric Granger, Alessandro Lameiras Koerich
Facial expressions are one of the most powerful ways for depicting specific patterns in human behavior and describing human emotional state. Despite the impressive advances of affe…
Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty
Soufiane Belharbi, Jérôme Rony, Jose Dolz +3
Weakly-supervised learning (WSL) has recently triggered substantial interest as it mitigates the lack of pixel-wise annotations. Given global image labels, WSL methods yield pixel-…
Deep DA for Ordinal Regression of Pain Intensity Estimation Using Weakly-Labeled Videos
Gnana Praveen R, Eric Granger, Patrick Cardinal
Automatic estimation of pain intensity from facial expressions in videos has an immense potential in health care applications. However, domain adaptation (DA) is needed to alleviat…
Deep Domain Adaptation for Ordinal Regression of Pain Intensity Estimation Using Weakly-Labelled Videos
R. Gnana Praveen, Eric Granger, Patrick Cardinal
Estimation of pain intensity from facial expressions captured in videos has an immense potential for health care applications. Given the challenges related to subjective variations…
A Flow-Guided Mutual Attention Network for Video-Based Person Re-Identification
Madhu Kiran, Amran Bhuiyan, Louis-Antoine Blais-Morin +3
Person Re-Identification (ReID) is a challenging problem in many video analytics and surveillance applications, where a person's identity must be associated across a distributed no…
Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification
Djebril Mekhazni, Amran Bhuiyan, George Ekladious +1
Person re-identification (ReID) remains a challenging task in many real-word video analytics and surveillance applications, even though state-of-the-art accuracy has improved consi…
Unsupervised Multi-Target Domain Adaptation Through Knowledge Distillation
Le Thanh Nguyen-Meidine, Atif Belal, Madhu Kiran +3
Unsupervised domain adaptation (UDA) seeks to alleviate the problem of domain shift between the distribution of unlabeled data from the target domain w.r.t. labeled data from the s…
Laplacian Regularized Few-Shot Learning
Imtiaz Masud Ziko, Jose Dolz, Eric Granger +1
We propose a transductive Laplacian-regularized inference for few-shot tasks. Given any feature embedding learned from the base classes, we minimize a quadratic binary-assignment f…
Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation
Le Thanh Nguyen-Meidine, Eric Granger, Madhu Kiran +2
Currently, the divergence in distributions of design and operational data, and large computational complexity are limiting factors in the adoption of CNNs in real-world application…
Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision
Hoel Kervadec, Jose Dolz, Shanshan Wang +2
We propose a novel weakly supervised learning segmentation based on several global constraints derived from box annotations. Particularly, we leverage a classical tightness prior t…
A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
Malik Boudiaf, Jérôme Rony, Imtiaz Masud Ziko +4
Recently, substantial research efforts in Deep Metric Learning (DML) focused on designing complex pairwise-distance losses, which require convoluted schemes to ease optimization, s…
Dual-Triplet Metric Learning for Unsupervised Domain Adaptation in Video-Based Face Recognition
George Ekladious, Hugo Lemoine, Eric Granger +2
The scalability and complexity of deep learning models remains a key issue in many of visual recognition applications like, e.g., video surveillance, where fine tuning with labeled…