Search Results for author: Marco Pedersoli

Found 38 papers, 19 papers with code

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

Diversified Multi-prototype Representation for Semi-supervised Segmentation

no code implementations16 Nov 2021 Jizong Peng, Christian Desrosiers, Marco Pedersoli

This work considers semi-supervised segmentation as a dense prediction problem based on prototype vector correlation and proposes a simple way to represent each segmentation class with multiple prototypes.

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

Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels

no code implementations NeurIPS 2021 Jizong Peng, Ping Wang, Chrisitian Desrosiers, Marco Pedersoli

Pre-training a recognition model with contrastive learning on a large dataset of unlabeled data has shown great potential to boost the performance of a downstream task, e. g., image classification.

Contrastive Learning Image Classification +2

Context-aware virtual adversarial training for anatomically-plausible segmentation

no code implementations12 Jul 2021 Ping Wang, Jizong Peng, Marco Pedersoli, Yuanfeng Zhou, Caiming Zhang, Christian Desrosiers

Despite their outstanding accuracy, semi-supervised segmentation methods based on deep neural networks can still yield predictions that are considered anatomically impossible by clinicians, for instance, containing holes or disconnected regions.

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

Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization

1 code implementation8 Mar 2021 Jizong Peng, Marco Pedersoli, Christian Desrosiers

In this method, we maximize the MI for intermediate feature embeddings that are taken from both the encoder and decoder of a segmentation network.

Medical Image Segmentation Semantic Segmentation

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

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

Self-paced and self-consistent co-training for semi-supervised image segmentation

1 code implementation31 Oct 2020 Ping Wang, Jizong Peng, Marco Pedersoli, Yuanfeng Zhou, Caiming Zhang, Christian Desrosiers

Moreover, to encourage predictions from different networks to be both consistent and confident, we enhance this generalized JSD loss with an uncertainty regularizer based on entropy.

Semantic Segmentation

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

Semi-supervised few-shot learning for medical image segmentation

no code implementations18 Mar 2020 Abdur R Feyjie, Reza Azad, Marco Pedersoli, Claude Kauffman, Ismail Ben Ayed, Jose Dolz

To handle this new learning paradigm, we propose to include surrogate tasks that can leverage very powerful supervisory signals --derived from the data itself-- for semantic feature learning.

Few-Shot Learning Lesion Segmentation +2

On the Texture Bias for Few-Shot CNN Segmentation

1 code implementation9 Mar 2020 Reza Azad, Abdur R Fayjie, Claude Kauffman, Ismail Ben Ayed, Marco Pedersoli, Jose Dolz

Despite the initial belief that Convolutional Neural Networks (CNNs) are driven by shapes to perform visual recognition tasks, recent evidence suggests that texture bias in CNNs provides higher performing models when learning on large labeled training datasets.

Few-Shot Semantic Segmentation Semantic Segmentation

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

A Computing Kernel for Network Binarization on PyTorch

1 code implementation11 Nov 2019 Xianda Xu, Marco Pedersoli

Deep Neural Networks have now achieved state-of-the-art results in a wide range of tasks including image classification, object detection and so on.

Binarization Image Classification +2

Information based Deep Clustering: An experimental study

no code implementations3 Oct 2019 Jizong Peng, Christian Desrosiers, Marco Pedersoli

The second, named Invariant Information Clustering (IIC), maximizes the mutual information between the clustering of a sample and its geometrically transformed version.

Deep Clustering

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

Adversarial Learning of General Transformations for Data Augmentation

no code implementations ICLR Workshop LLD 2019 Saypraseuth Mounsaveng, David Vazquez, Ismail Ben Ayed, Marco Pedersoli

Data augmentation (DA) is fundamental against overfitting in large convolutional neural networks, especially with a limited training dataset.

Data Augmentation

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

Deep Co-Training for Semi-Supervised Image Segmentation

2 code implementations27 Mar 2019 Jizong Peng, Guillermo Estrada, Marco Pedersoli, Christian Desrosiers

In this paper, we aim to improve the performance of semantic image segmentation in a semi-supervised setting in which training is effectuated with a reduced set of annotated images and additional non-annotated images.

Semantic Segmentation

Deep clustering: On the link between discriminative models and K-means

1 code implementation9 Oct 2018 Mohammed Jabi, Marco Pedersoli, Amar Mitiche, Ismail Ben Ayed

Typically, they use multinomial logistic regression posteriors and parameter regularization, as is very common in supervised learning.

Deep Clustering Image Clustering

An Attention Model for group-level emotion recognition

1 code implementation9 Jul 2018 Aarush Gupta, Dakshit Agrawal, Hardik Chauhan, Jose Dolz, Marco Pedersoli

In this paper we propose a new approach for classifying the global emotion of images containing groups of people.

Emotion Recognition

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

DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers

1 code implementation15 Jun 2016 Amir Ghodrati, Ali Diba, Marco Pedersoli, Tinne Tuytelaars, Luc van Gool

In this paper, a new method for generating object and action proposals in images and videos is proposed.

Learning Where to Position Parts in 3D

no code implementations ICCV 2015 Marco Pedersoli, Tinne Tuytelaars

In this paper we propose a new method for the detection and pose estimation of 3D objects, that does not use any 3D CAD model or other 3D information.

Object Detection Pose Estimation

Towards Automatic Image Editing: Learning to See another You

no code implementations26 Nov 2015 Amir Ghodrati, Xu Jia, Marco Pedersoli, Tinne Tuytelaars

Learning the distribution of images in order to generate new samples is a challenging task due to the high dimensionality of the data and the highly non-linear relations that are involved.

Image Generation

DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

1 code implementation ICCV 2015 Amir Ghodrati, Ali Diba, Marco Pedersoli, Tinne Tuytelaars, Luc van Gool

We generate hypotheses in a sliding-window fashion over different activation layers and show that the final convolutional layers can find the object of interest with high recall but poor localization due to the coarseness of the feature maps.

Weakly Supervised Object Detection With Convex Clustering

no code implementations CVPR 2015 Hakan Bilen, Marco Pedersoli, Tinne Tuytelaars

However, as learning appearance and localization are two interconnected tasks, the optimization is not convex and the procedure can easily get stuck in a poor local minimum, the algorithm "misses" the object in some images.

Weakly Supervised Object Detection

Object Classification with Adaptable Regions

no code implementations CVPR 2014 Hakan Bilen, Marco Pedersoli, Vinay P. Namboodiri, Tinne Tuytelaars, Luc van Gool

In classification of objects substantial work has gone into improving the low level representation of an image by considering various aspects such as different features, a number of feature pooling and coding techniques and considering different kernels.

Classification General Classification

Using a Deformation Field Model for Localizing Faces and Facial Points under Weak Supervision

no code implementations CVPR 2014 Marco Pedersoli, Tinne Tuytelaars, Luc van Gool

Additionally, without any facial point annotation at the level of individual training images, our method can localize facial points with an accuracy similar to fully supervised approaches.

Face Detection

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