Search Results for author: Soufiane Belharbi

Found 13 papers, 12 papers with code

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

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

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

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

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

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

Neural Networks Regularization Through Representation Learning

1 code implementation13 Jul 2018 Soufiane Belharbi

In this thesis, we tackle the neural network overfitting issue from a representation learning perspective by considering the situation where few training samples are available which is the case of many real world applications.

Data Augmentation Representation Learning +1

Deep Neural Networks Regularization for Structured Output Prediction

1 code implementation28 Apr 2015 Soufiane Belharbi, Romain Hérault, Clément Chatelain, Sébastien Adam

The motivation of this work is to learn the output dependencies that may lie in the output data in order to improve the prediction accuracy.

Facial Landmark Detection

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