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20192026
most citedBoosting Deep Open World Recognition by Clustering

23 citations · 63 across the 20 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.CV2022

CoMFormer: Continual Learning in Semantic and Panoptic Segmentation

Fabio Cermelli, Matthieu Cord, Arthur Douillard

Continual learning for segmentation has recently seen increasing interest. However, all previous works focus on narrow semantic segmentation and disregard panoptic segmentation, an…

cs.CV2022★ 3 cited

Detecting the unknown in Object Detection

Dario Fontanel, Matteo Tarantino, Fabio Cermelli +1

Object detection methods have witnessed impressive improvements in the last years thanks to the design of novel neural network architectures and the availability of large scale dat…

cs.CV2022★ 3 cited

Modeling Missing Annotations for Incremental Learning in Object Detection

Fabio Cermelli, Antonino Geraci, Dario Fontanel +1

Despite the recent advances in the field of object detection, common architectures are still ill-suited to incrementally detect new categories over time. They are vulnerable to cat…

cs.CV2022★ 3 cited

FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

Lidia Fantauzzo, Eros Fanì, Debora Caldarola +4

Semantic Segmentation is essential to make self-driving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. Howe…

cs.CV2022★ 14 cited

Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation

Fabio Cermelli, Massimiliano Mancini, Samuel Rota Buló +2

Deep neural networks have enabled major progresses in semantic segmentation. However, even the most advanced neural architectures suffer from important limitations. First, they are…