Search Results for author: Fabio Cermelli

Found 14 papers, 12 papers with code

Detecting the unknown in Object Detection

no code implementations24 Aug 2022 Dario Fontanel, Matteo Tarantino, Fabio Cermelli, Barbara Caputo

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 datasets.

object-detection Object Detection

FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

1 code implementation28 Feb 2022 Lidia Fantauzzo, Eros Fani', Debora Caldarola, Antonio Tavera, Fabio Cermelli, Marco Ciccone, Barbara Caputo

For similar reasons, Federated Learning has been recently introduced as a new machine learning paradigm aiming to learn a global model while preserving privacy and leveraging data on millions of remote devices.

Autonomous Driving Domain Generalization +3

Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation

1 code implementation31 Jan 2022 Fabio Cermelli, Massimiliano Mancini, Samuel Rota Buló, Elisa Ricci, Barbara Caputo

To tackle these issues, we introduce a novel incremental class learning approach for semantic segmentation taking into account a peculiar aspect of this task: since each training step provides annotation only for a subset of all possible classes, pixels of the background class exhibit a semantic shift.

Weakly supervised segmentation Weakly-Supervised Semantic Segmentation

A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images

1 code implementation7 Dec 2021 Edoardo Arnaudo, Fabio Cermelli, Antonio Tavera, Claudio Rossi, Barbara Caputo

Incremental learning represents a crucial task in aerial image processing, especially given the limited availability of large-scale annotated datasets.

Incremental Learning Knowledge Distillation +3

Incremental Learning in Semantic Segmentation from Image Labels

1 code implementation CVPR 2022 Fabio Cermelli, Dario Fontanel, Antonio Tavera, Marco Ciccone, Barbara Caputo

As opposed to existing approaches, that need to generate pseudo-labels offline, we use an auxiliary classifier, trained with image-level labels and regularized by the segmentation model, to obtain pseudo-supervision online and update the model incrementally.

Incremental Learning Semantic Segmentation

Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation

1 code implementation22 Oct 2021 Antonio Tavera, Fabio Cermelli, Carlo Masone, Barbara Caputo

The pixel-wise adversarial training is assisted by a novel sample selection procedure, that handles the imbalance between source and target data, and a knowledge distillation strategy, that avoids overfitting towards the few target images.

Autonomous Driving Cross-Domain Few-Shot +3

On the Challenges of Open World Recognitionunder Shifting Visual Domains

1 code implementation9 Jul 2021 Dario Fontanel, Fabio Cermelli, Massimiliano Mancini, Barbara Caputo

Robotic visual systems operating in the wild must act in unconstrained scenarios, under different environmental conditions while facing a variety of semantic concepts, including unknown ones.

Domain Generalization Object Recognition

Detecting Anomalies in Semantic Segmentation with Prototypes

1 code implementation1 Jun 2021 Dario Fontanel, Fabio Cermelli, Massimiliano Mancini, Barbara Caputo

Current state of the art of anomaly segmentation uses generative models, exploiting their incapability to reconstruct patterns unseen during training.

Semantic Segmentation

A Closer Look at Self-training for Zero-Label Semantic Segmentation

1 code implementation21 Apr 2021 Giuseppe Pastore, Fabio Cermelli, Yongqin Xian, Massimiliano Mancini, Zeynep Akata, Barbara Caputo

Being able to segment unseen classes not observed during training is an important technical challenge in deep learning, because of its potential to reduce the expensive annotation required for semantic segmentation.

Semantic Segmentation

Prototype-based Incremental Few-Shot Semantic Segmentation

1 code implementation30 Nov 2020 Fabio Cermelli, Massimiliano Mancini, Yongqin Xian, Zeynep Akata, Barbara Caputo

Semantic segmentation models have two fundamental weaknesses: i) they require large training sets with costly pixel-level annotations, and ii) they have a static output space, constrained to the classes of the training set.

Few-Shot Semantic Segmentation Incremental Learning +2

Boosting Deep Open World Recognition by Clustering

no code implementations20 Apr 2020 Dario Fontanel, Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci, Barbara Caputo

While convolutional neural networks have brought significant advances in robot vision, their ability is often limited to closed world scenarios, where the number of semantic concepts to be recognized is determined by the available training set.

Incremental Learning Open Set Learning

Modeling the Background for Incremental Learning in Semantic Segmentation

1 code implementation CVPR 2020 Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci, Barbara Caputo

Current strategies fail on this task because they do not consider a peculiar aspect of semantic segmentation: since each training step provides annotation only for a subset of all possible classes, pixels of the background class (i. e. pixels that do not belong to any other classes) exhibit a semantic distribution shift.

Continual Learning Disjoint 10-1 +8

The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots

1 code implementation1 Apr 2019 Fabio Cermelli, Massimiliano Mancini, Elisa Ricci, Barbara Caputo

Deep networks have brought significant advances in robot perception, enabling to improve the capabilities of robots in several visual tasks, ranging from object detection and recognition to pose estimation, semantic scene segmentation and many others.

object-detection Object Detection +2

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