Search Results for author: Eric Granger

Found 67 papers, 35 papers with code

Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network

no code implementations20 May 2022 Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger, Huiyu Zhou

Skin lesion detection in dermoscopic images is essential in the accurate and early diagnosis of skin cancer by a computerized apparatus.

Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images

no code implementations12 May 2022 Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger, Jocelyn Chanussot, Jie Yang

In IPSSD, single-shot detector is adopted combined with an image pyramid network to extract semantically strong features for generating candidate regions.

Small Object Detection

Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth Boxes

1 code implementation1 Apr 2022 Akhil Meethal, Marco Pedersoli, Zhongwen Zhu, Francisco Perdigon Romero, Eric Granger

Semi- and weakly-supervised learning have recently attracted considerable attention in the object detection literature since they can alleviate the cost of annotation needed to successfully train deep learning models.

Data Augmentation Weakly Supervised Object Detection

A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion Recognition

1 code implementation28 Mar 2022 Gnana Praveen Rajasekar, Wheidima Carneiro de Melo, Nasib Ullah, Haseeb Aslam, Osama Zeeshan, Théo Denorme, Marco Pedersoli, Alessandro Koerich, Simon Bacon, Patrick Cardinal, Eric Granger

Specifically, we propose a joint cross-attention model that relies on the complementary relationships to extract the salient features across A-V modalities, allowing for accurate prediction of continuous values of valence and arousal.

Multimodal Emotion Recognition

Facial Expression Analysis Using Decomposed Multiscale Spatiotemporal Networks

no code implementations21 Mar 2022 Wheidima Carneiro de Melo, Eric Granger, Miguel Bordallo Lopez

Our extensive experiments on challenging datasets show that the DMSN-C block is effective for depression detection, whereas the DMSN-A block is efficient for pain estimation.

Depression Detection

Dynamic Template Selection Through Change Detection for Adaptive Siamese Tracking

no code implementations7 Mar 2022 Madhu Kiran, Le Thanh Nguyen-Meidine, Rajat Sahay, Rafael Menelau Oliveira E Cruz, Louis-Antoine Blais-Morin, Eric Granger

Results indicate that integrating our proposed method into state-of-art adaptive Siamese trackers can increase the potential benefits of a template update strategy, and significantly improve performance.

Change Detection Incremental Learning +2

Generative Target Update for Adaptive Siamese Tracking

no code implementations21 Feb 2022 Madhu Kiran, Le Thanh Nguyen-Meidine, Rajat Sahay, Rafael Menelau Oliveira E Cruz, Louis-Antoine Blais-Morin, Eric Granger

This paper proposes a model adaptation method for Siamese trackers that uses a generative model to produce a synthetic template from the object search regions of several previous frames, rather than directly using the tracker output.

Change Detection

Cross Attentional Audio-Visual Fusion for Dimensional Emotion Recognition

1 code implementation9 Nov 2021 Gnana Praveen R, Eric Granger, Patrick Cardinal

Results indicate that our cross-attentional A-V fusion model is a cost-effective approach that outperforms state-of-the-art fusion approaches.

Multimodal Emotion Recognition

F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling

1 code implementation15 Sep 2021 Soufiane Belharbi, Aydin Sarraf, Marco Pedersoli, Ismail Ben Ayed, Luke McCaffrey, Eric Granger

Interpolation is required to restore full size CAMs, yet it does not consider the statistical properties of objects, such as color and texture, leading to activations with inconsistent boundaries, and inaccurate localizations.

Weakly-Supervised Object Localization

Transductive Few-Shot Learning: Clustering is All You Need?

1 code implementation16 Jun 2021 Imtiaz Masud Ziko, Malik Boudiaf, Jose Dolz, Eric Granger, Ismail Ben Ayed

Surprisingly, we found that even standard clustering procedures (e. g., K-means), which correspond to particular, non-regularized cases of our general model, already achieve competitive performances in comparison to the state-of-the-art in few-shot learning.

Few-Shot Learning

Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain Transfer

1 code implementation13 Apr 2021 Le Thanh Nguyen-Meidine, Madhu Kiran, Marco Pedersoli, Jose Dolz, Louis-Antoine Blais-Morin, Eric Granger

Recent advances in unsupervised domain adaptation have significantly improved the recognition accuracy of CNNs by alleviating the domain shift between (labeled) source and (unlabeled) target data distributions.

Incremental Learning Knowledge Distillation +4

Holistic Guidance for Occluded Person Re-Identification

no code implementations13 Apr 2021 Madhu Kiran, R Gnana Praveen, Le Thanh Nguyen-Meidine, Soufiane Belharbi, Louis-Antoine Blais-Morin, Eric Granger

Hence, our proposed student-teacher framework is trained to address the occlusion problem by matching the distributions of between- and within-class distances (DCDs) of occluded samples with that of holistic (non-occluded) samples, thereby using the latter as a soft labeled reference to learn well separated DCDs.

Denoising Person Re-Identification +1

Weakly Supervised Learning for Facial Behavior Analysis : A Review

no code implementations25 Jan 2021 Gnana Praveen R, Eric Granger, Patrick Cardinal

In this paper, we provide a comprehensive review of weakly supervised learning (WSL) approaches for facial behavior analysis with both categorical as well as dimensional labels along with the challenges and potential research directions associated with it.

Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains

no code implementations18 Jan 2021 Le Thanh Nguyen-Meidine, Atif Belal, Madhu Kiran, Jose Dolz, Louis-Antoine Blais-Morin, Eric Granger

Our proposed approach is compared against state-of-the-art methods for compression and STDA of CNNs on the Office31 and ImageClef-DA image classification datasets.

Knowledge Distillation Person Re-Identification +1

Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies

1 code implementation26 Dec 2020 Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger, Huiyu Zhou, Ruili Wang, M. Emre Celebi, Jie Yang

However, there is a lack of comprehensive review in this field, especially lack of a collection of GANs loss-variant, evaluation metrics, remedies for diverse image generation, and stable training.

Image-to-Image Translation Translation

Augmented Lagrangian Adversarial Attacks

2 code implementations ICCV 2021 Jérôme Rony, Eric Granger, Marco Pedersoli, Ismail Ben Ayed

Our attack enjoys the generality of penalty methods and the computational efficiency of distance-customized algorithms, and can be readily used for a wide set of distances.

Adversarial Attack

Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks

no code implementations18 Nov 2020 Thomas Teixeira, Eric Granger, Alessandro Lameiras Koerich

In this paper, we investigate the suitability of state-of-the-art deep learning architectures based on convolutional neural networks (CNNs) for continuous emotion recognition using long video sequences captured in-the-wild.

Emotion Recognition Facial Expression Recognition +1

Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty

2 code implementations14 Nov 2020 Soufiane Belharbi, Jérôme Rony, Jose Dolz, Ismail Ben Ayed, Luke McCaffrey, Eric Granger

We propose novel regularization terms, which enable the model to seek both non-discriminative and discriminative regions, while discouraging unbalanced segmentations.

General Classification Weakly supervised segmentation

Temporal Stochastic Softmax for 3D CNNs: An Application in Facial Expression Recognition

no code implementations10 Nov 2020 Théo Ayral, Marco Pedersoli, Simon Bacon, Eric Granger

The proposed softmax strategy provides several advantages: a reduced computational complexity due to efficient clip sampling, and an improved accuracy since temporal weighting focuses on more relevant clips during both training and inference.

Facial Expression Recognition

Deep DA for Ordinal Regression of Pain Intensity Estimation Using Weakly-Labeled Videos

no code implementations28 Oct 2020 Gnana Praveen R, Eric Granger, Patrick Cardinal

The WSDA-OR model enforces ordinal relationships among the intensity levels as-signed to the target sequences, and associates multiple relevant frames to sequence-level labels (instead of a single frame).

Domain Adaptation

Deep Active Learning for Joint Classification & Segmentation with Weak Annotator

1 code implementation10 Oct 2020 Soufiane Belharbi, Ismail Ben Ayed, Luke McCaffrey, Eric Granger

CNN visualization and interpretation methods, like class-activation maps (CAMs), are typically used to highlight the image regions linked to class predictions.

Active Learning Classification +2

A Flow-Guided Mutual Attention Network for Video-Based Person Re-Identification

no code implementations9 Aug 2020 Madhu Kiran, Amran Bhuiyan, Louis-Antoine Blais-Morin, Mehrsan Javan, Ismail Ben Ayed, Eric Granger

Our Mutual Attention network relies on the joint spatial attention between image and optical flow features maps to activate a common set of salient features across them.

Optical Flow Estimation Video-Based Person Re-Identification

Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification

2 code implementations ECCV 2020 Djebril Mekhazni, Amran Bhuiyan, George Ekladious, Eric Granger

We argue that for pair-wise matchers that rely on metric learning, e. g., Siamese networks for person ReID, the unsupervised domain adaptation (UDA) objective should consist in aligning pair-wise dissimilarity between domains, rather than aligning feature representations.

Data Augmentation Metric Learning +2

Laplacian Regularized Few-Shot Learning

1 code implementation28 Jun 2020 Imtiaz Masud Ziko, Jose Dolz, Eric Granger, Ismail Ben Ayed

Our transductive inference does not re-train the base model, and can be viewed as a graph clustering of the query set, subject to supervision constraints from the support set.

Few-Shot Image Classification Graph Clustering

Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation

2 code implementations16 May 2020 Le Thanh Nguyen-Meidine, Eric Granger, Madhu Kiran, Jose Dolz, Louis-Antoine Blais-Morin

In both datasets, results indicate that our method can achieve the highest level of accuracy while requiring a comparable or lower time complexity.

Knowledge Distillation Person Re-Identification +1

A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses

1 code implementation ECCV 2020 Malik Boudiaf, Jérôme Rony, Imtiaz Masud Ziko, Eric Granger, Marco Pedersoli, Pablo Piantanida, Ismail Ben Ayed

Second, we show that, more generally, minimizing the cross-entropy is actually equivalent to maximizing the mutual information, to which we connect several well-known pairwise losses.

Ranked #7 on Metric Learning on In-Shop (using extra training data)

Metric Learning

Dual-Triplet Metric Learning for Unsupervised Domain Adaptation in Video-Based Face Recognition

no code implementations11 Feb 2020 George Ekladious, Hugo Lemoine, Eric Granger, Kaveh Kamali, Salim Moudache

To this end, a dual-triplet loss is introduced for metric learning, where two triplets are constructed using video data from a source camera, and a new target camera.

Camera Calibration Face Recognition +3

Convolutional STN for Weakly Supervised Object Localization

1 code implementation3 Dec 2019 Akhil Meethal, Marco Pedersoli, Soufiane Belharbi, Eric Granger

Weakly supervised object localization is a challenging task in which the object of interest should be localized while learning its appearance.

Weakly-Supervised Object Localization

Non-parametric Uni-modality Constraints for Deep Ordinal Classification

1 code implementation25 Nov 2019 Soufiane Belharbi, Ismail Ben Ayed, Luke McCaffrey, Eric Granger

We propose a new constrained-optimization formulation for deep ordinal classification, in which uni-modality of the label distribution is enforced implicitly via a set of inequality constraints over all the pairs of adjacent labels.

Classification General Classification +1

On the Interaction Between Deep Detectors and Siamese Trackers in Video Surveillance

no code implementations31 Oct 2019 Madhu Kiran, Vivek Tiwari, Le Thanh Nguyen-Meidine, Eric Granger

However, bounding boxes provided by a state-of-the-art detector are noisy, due to changes in appearance, background and occlusion, which can cause the tracker to drift.

Change Detection Visual Object Tracking

Cross-Domain Face Synthesis using a Controllable GAN

1 code implementation31 Oct 2019 Fania Mokhayeri, Kaveh Kamali, Eric Granger

This allows generating realistic synthetic face images that reflects capture conditions in the target domain while controlling the GAN output to generate faces under desired pose conditions.

Data Augmentation Face Generation +2

Deep Weakly-Supervised Domain Adaptation for Pain Localization in Videos

no code implementations17 Oct 2019 Gnana Praveen R, Eric Granger, Patrick Cardinal

Automatic pain assessment has an important potential diagnostic value for populations that are incapable of articulating their pain experiences.

Domain Adaptation Frame +1

A Paired Sparse Representation Model for Robust Face Recognition from a Single Sample

no code implementations5 Oct 2019 Fania Mokhayeri, Eric Granger

In order to account for non-linear variations due to pose, a paired sparse representation model is introduced allowing for joint use of variational information and synthetic face images.

Face Recognition Robust Face Recognition +1

Emotion Recognition with Spatial Attention and Temporal Softmax Pooling

no code implementations2 Oct 2019 Masih Aminbeidokhti, Marco Pedersoli, Patrick Cardinal, Eric Granger

Video-based emotion recognition is a challenging task because it requires to distinguish the small deformations of the human face that represent emotions, while being invariant to stronger visual differences due to different identities.

Emotion Recognition

Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Comparative Study

1 code implementation8 Sep 2019 Jérôme Rony, Soufiane Belharbi, Jose Dolz, Ismail Ben Ayed, Luke McCaffrey, Eric Granger

Four key challenges are identified for the application of deep WSOL methods in histology -- under/over activation of CAMs, sensitivity to thresholding, and model selection.

General Classification Model Selection +1

Min-max Entropy for Weakly Supervised Pointwise Localization

1 code implementation25 Jul 2019 Soufiane Belharbi, Jérôme Rony, Jose Dolz, Ismail Ben Ayed, Luke McCaffrey, Eric Granger

Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain.

Weakly-Supervised Object Localization

Exploiting Prunability for Person Re-Identification

no code implementations4 Jul 2019 Hugo Masson, Amran Bhuiyan, Le Thanh Nguyen-Meidine, Mehrsan Javan, Parthipan Siva, Ismail Ben Ayed, Eric Granger

Then, these techniques are analysed according to their pruningcriteria and strategy, and according to different scenarios for exploiting pruningmethods to fine-tuning networks to target domains.

Person Re-Identification

Audio-Visual Kinship Verification

no code implementations24 Jun 2019 Xiaoting Wu, Eric Granger, Xiaoyi Feng

Then, early and late fusion methods are evaluated on the TALKIN dataset for the study of kinship verification with both face and voice modalities.

Face Verification

Progressive Gradient Pruning for Classification, Detection and DomainAdaptation

1 code implementation20 Jun 2019 Le Thanh Nguyen-Meidine, Eric Granger, Madhu Kiran, Louis-Antoine Blais-Morin, Marco Pedersoli

Although deep neural networks (NNs) have achievedstate-of-the-art accuracy in many visual recognition tasks, the growing computational complexity and energy con-sumption of networks remains an issue, especially for ap-plications on platforms with limited resources and requir-ing real-time processing.

Classification General Classification +1

Variational Fair Clustering

1 code implementation19 Jun 2019 Imtiaz Masud Ziko, Eric Granger, Jing Yuan, Ismail Ben Ayed

We derive a general tight upper bound based on a concave-convex decomposition of our fairness term, its Lipschitz-gradient property and the Pinsker's inequality.


Curriculum semi-supervised segmentation

1 code implementation10 Apr 2019 Hoel Kervadec, Jose Dolz, Eric Granger, Ismail Ben Ayed

This study investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of a target region.

Left Ventricle Segmentation Semi-Supervised Semantic Segmentation

Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions

1 code implementation8 Apr 2019 Hoel Kervadec, Jose Dolz, Jing Yuan, Christian Desrosiers, Eric Granger, Ismail Ben Ayed

While sub-optimality is not guaranteed for non-convex problems, this result shows that log-barrier extensions are a principled way to approximate Lagrangian optimization for constrained CNNs via implicit dual variables.

Semantic Segmentation Stochastic Optimization +1

Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses

3 code implementations23 Nov 2018 Jérôme Rony, Luiz G. Hafemann, Luiz S. Oliveira, Ismail Ben Ayed, Robert Sabourin, Eric Granger

Research on adversarial examples in computer vision tasks has shown that small, often imperceptible changes to an image can induce misclassification, which has security implications for a wide range of image processing systems.

Scalable Laplacian K-modes

1 code implementation NeurIPS 2018 Imtiaz Masud Ziko, Eric Granger, Ismail Ben Ayed

Furthermore, we show that the density modes can be obtained as byproducts of the assignment variables via simple maximum-value operations whose additional computational cost is linear in the number of data points.

A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications

no code implementations10 Sep 2018 Le Thanh Nguyen-Meidine, Eric Granger, Madhu Kiran, Louis-Antoine Blais-Morin

Detecting faces and heads appearing in video feeds are challenging tasks in real-world video surveillance applications due to variations in appearance, occlusions and complex backgrounds.

Head Detection

Multi-region segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

no code implementations28 May 2018 Jose Dolz, Xiaopan Xu, Jerome Rony, Jing Yuan, Yang Liu, Eric Granger, Christian Desrosiers, Xi Zhang, Ismail Ben Ayed, Hongbing Lu

Precise segmentation of bladder walls and tumor regions is an essential step towards non-invasive identification of tumor stage and grade, which is critical for treatment decision and prognosis of patients with bladder cancer (BC).

Constrained-CNN losses for weakly supervised segmentation

4 code implementations12 May 2018 Hoel Kervadec, Jose Dolz, Meng Tang, Eric Granger, Yuri Boykov, Ismail Ben Ayed

To the best of our knowledge, the method of [Pathak et al., 2015] is the only prior work that addresses deep CNNs with linear constraints in weakly supervised segmentation.

Medical Image Segmentation Weakly supervised segmentation +1

Deep Learning Architectures for Face Recognition in Video Surveillance

no code implementations27 Feb 2018 Saman Bashbaghi, Eric Granger, Robert Sabourin, Mostafa Parchami

In video-based FR systems, facial models of target individuals are designed a priori during enrollment using a limited number of reference still images or video data.

Face Recognition

Domain-Specific Face Synthesis for Video Face Recognition from a Single Sample Per Person

1 code implementation6 Jan 2018 Fania Mokhayeri, Eric Granger, Guillaume-Alexandre Bilodeau

A compact set of synthetic faces is generated that resemble individuals of interest under the capture conditions relevant to the OD.

3D Reconstruction Face Generation +1

Modeling Information Flow Through Deep Neural Networks

no code implementations29 Nov 2017 Ahmad Chaddad, Behnaz Naisiri, Marco Pedersoli, Eric Granger, Christian Desrosiers, Matthew Toews

This paper proposes a principled information theoretic analysis of classification for deep neural network structures, e. g. convolutional neural networks (CNN).

Classification General Classification +1

Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems

no code implementations6 Oct 2017 Marc-André Carbonneau, Eric Granger, Ghyslain Gagnon

In such cases, active learning (AL) can reduce labeling costs for training a classifier by querying the expert to provide the labels of most informative instances.

Active Learning General Classification +2

Robust Face Tracking using Multiple Appearance Models and Graph Relational Learning

1 code implementation29 Jun 2017 Tanushri Chakravorty, Guillaume-Alexandre Bilodeau, Eric Granger

This paper addresses the problem of appearance matching across different challenges while doing visual face tracking in real-world scenarios.

Relational Reasoning

Progressive Boosting for Class Imbalance

no code implementations5 Jun 2017 Roghayeh Soleymani, Eric Granger, Giorgio Fumera

Results show that PBoost can outperform state of the art techniques in terms of both accuracy and complexity over different levels of imbalance and overlap between classes.

Ensemble Learning

Tracking using Numerous Anchor points

2 code implementations7 Feb 2017 Tanushri Chakravorty, Guillaume-Alexandre Bilodeau, Eric Granger

In this paper, an online adaptive model-free tracker is proposed to track single objects in video sequences to deal with real-world tracking challenges like low-resolution, object deformation, occlusion and motion blur.

Object Localization Visual Tracking

Multiple Instance Learning: A Survey of Problem Characteristics and Applications

1 code implementation11 Dec 2016 Marc-André Carbonneau, Veronika Cheplygina, Eric Granger, Ghyslain Gagnon

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag.

Document Classification Multiple Instance Learning

Feature Learning from Spectrograms for Assessment of Personality Traits

no code implementations4 Oct 2016 Marc-André Carbonneau, Eric Granger, Yazid Attabi, Ghyslain Gagnon

The number of features, and difficulties linked to the feature extraction process are greatly reduced as only one type of descriptors is used, for which the 6 parameters can be tuned automatically.

Offline Signature-Based Fuzzy Vault (OSFV: Review and New Results

no code implementations18 Aug 2014 George S. Eskander, Robert Sabourin, Eric Granger

An offline signature-based fuzzy vault (OSFV) is a bio-cryptographic implementation that uses handwritten signature images as biometrics instead of traditional passwords to secure private cryptographic keys.

Automatic Image Registration in Infrared-Visible Videos using Polygon Vertices

no code implementations17 Mar 2014 Tanushri Chakravorty, Guillaume-Alexandre Bilodeau, Eric Granger

To achieve a global affine transformation that maximises the overlapping of infrared and visible foreground pixels, the matched keypoints of each local shape polygon are stored temporally in a buffer for a few number of frames.

Frame Image Registration

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