176 citations · 538 across the 62 of their papers we have counts for
14 papers · 1 filter
Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery
Issam Laradji, Pau Rodriguez, Freddie Kalaitzis +4
Cattle farming is responsible for 8.8\% of greenhouse gas emissions worldwide. In addition to the methane emitted due to their digestive process, the growing need for grazing areas…
Affinity LCFCN: Learning to Segment Fish with Weak Supervision
Issam Laradji, Alzayat Saleh, Pau Rodriguez +3
Aquaculture industries rely on the availability of accurate fish body measurements, e.g., length, width and mass. Manual methods that rely on physical tools like rulers are time an…
CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions
Vincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodriguez +12
In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems…
Synbols: Probing Learning Algorithms with Synthetic Datasets
Alexandre Lacoste, Pau Rodríguez, Frédéric Branchaud-Charron +7
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms. Enabling the design of datasets to te…
A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis
Alzayat Saleh, Issam H. Laradji, Dmitry A. Konovalov +3
Visual analysis of complex fish habitats is an important step towards sustainable fisheries for human consumption and environmental protection. Deep Learning methods have shown gre…
Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients
Keegan Lensink, Issam Laradji, Marco Law +4
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread into a global pandemic. A form of pneumonia, presenting as opacities with in a patient's lungs,…
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images
Issam Laradji, Pau Rodriguez, Frederic Branchaud-Charron +5
One of the key challenges in the battle against the Coronavirus (COVID-19) pandemic is to detect and quantify the severity of the disease in a timely manner. Computed tomographies…
A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images
Issam Laradji, Pau Rodriguez, Oscar Mañas +6
Coronavirus Disease 2019 (COVID-19) has spread aggressively across the world causing an existential health crisis. Thus, having a system that automatically detects COVID-19 in tomo…
LOOC: Localize Overlapping Objects with Count Supervision
Issam H. Laradji, Rafael Pardinas, Pau Rodriguez +1
Acquiring count annotations generally requires less human effort than point-level and bounding box annotations. Thus, we propose the novel problem setup of localizing objects in de…
Learning Data Augmentation with Online Bilevel Optimization for Image Classification
Saypraseuth Mounsaveng, Issam Laradji, Ismail Ben Ayed +2
Data augmentation is a key practice in machine learning for improving generalization performance. However, finding the best data augmentation hyperparameters requires domain knowle…
Adaptive Gradient Methods Converge Faster with Over-Parameterization (but you should do a line-search)
Sharan Vaswani, Issam Laradji, Frederik Kunstner +3
Adaptive gradient methods are typically used for training over-parameterized models. To better understand their behaviour, we study a simplistic setting -- smooth, convex losses wi…
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko +8
Continual learning studies agents that learn from streams of tasks without forgetting previous ones while adapting to new ones. Two recent continual-learning scenarios have opened…
Embedding Propagation: Smoother Manifold for Few-Shot Classification
Pau Rodríguez, Issam Laradji, Alexandre Drouin +1
Few-shot classification is challenging because the data distribution of the training set can be widely different to the test set as their classes are disjoint. This distribution sh…
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
Nicolas Loizou, Sharan Vaswani, Issam Laradji +1
We propose a stochastic variant of the classical Polyak step-size (Polyak, 1987) commonly used in the subgradient method. Although computing the Polyak step-size requires knowledge…