most citedA Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis

176 citations · 216 across the 6 of their papers we have counts for

collaborators

8 papers

cs.CV2020

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…

cs.CV2020176 cited

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…

eess.IV202017 cited

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…

eess.IV20201 cited

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…

cs.CV2020

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…

cs.CV201913 cited

Instance Segmentation with Point Supervision

Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro +2

Instance segmentation methods often require costly per-pixel labels. We propose a method that only requires point-level annotations. During training, the model only has access to a…