Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DLbased solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, videolevel, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.

No abstract

No abstract

We are thankful to Amedeo Setti (ProM Facility, Trentino Sviluppo) for developing the web-based applications and for managing the data collection and storage on the cluster of servers.We are thankful to the CARITRO Foundation for partially supporting this project and for establishing the Deep Learning Lab at the ProM Facility (Trentino Sviluppo). Appreciation is expressed to Filippo Degasperi for partially funding the Oxynet web-application development within the "Restitution Project". Author contributionAZ and AF conceived of the original idea and drafted the manuscript. AZ developed the theory and performed the computations. PR assisted AZ in the creation of the models and contributed to the interpretation of the results. A.F., V.M., L.P.T., D.A.L., F.Y.F., D.B., M.P., S.R.D. and L.M. supervised and carried out the experiments, contributed to sample preparation and results interpretation. All authors discussed the results and contributed to the final manuscript.

The topic of crowd modeling in computer vision usually assumes a single generic typology of crowd, which is very simplistic. In this paper we adopt a taxonomy that is widely accepted in sociology, focusing on a particular category, the spectator crowd, which is formed by people "interested in watching something specific that they came to see" [6]. This can be found at the stadiums, amphitheaters, cinema, etc. In particular, we propose a novel dataset, the Spectators Hockey (S-HOCK), which deals with 4 hockey matches during an international tournament. In the dataset, a massive annotation has been carried out, focusing on the spectators at different levels of details: at a higher level, people have been labeled depending on the team they are supporting and the fact that they know the people close to them; going to the lower levels, standard pose information has been considered (regarding the head, the body) but also fine grained actions such as hands on hips, clapping hands etc. The labeling focused on the game field also, permitting to relate what is going on in the match with the crowd behavior. This brought to more than 100 millions of annotations, useful for standard applications as people counting and head pose estimation but also for novel tasks as spectator categorization. For all of these we provide protocols and baseline results, encouraging further research.

A fundamental and challenging problem in deep learning is catastrophic forgetting, i.e. the tendency of neural networks to fail to preserve the knowledge acquired from old tasks when learning new tasks. This problem has been widely investigated in the research community and several Incremental Learning (IL) approaches have been proposed in the past years. While earlier works in computer vision have mostly focused on image classification and object detection, more recently some IL approaches for semantic segmentation have been introduced. These previous works showed that, despite its simplicity, knowledge distillation can be effectively employed to alleviate catastrophic forgetting. In this paper, we follow this research direction and, inspired by recent literature on contrastive learning, we propose a novel distillation framework, Uncertainty-aware Contrastive Distillation (UCD). In a nutshell, UCD is operated by introducing a novel distillation loss that takes into account all the images in a mini-batch, enforcing similarity between features associated to all the pixels from the same classes, and pulling apart those corresponding to pixels from different classes. In order to mitigate catastrophic forgetting, we contrast features of the new model with features extracted by a frozen model learned at the previous incremental step. Our experimental results demonstrate the advantage of the proposed distillation technique, which can be used in synergy with previous IL approaches, and leads to state-of-art performance on three commonly adopted benchmarks for incremental semantic segmentation. The code is available at https://github.com/ygjwd12345/UCD.

No abstract

Over the last few years, Unsupervised Domain Adaptation (UDA) techniques have acquired remarkable importance and popularity in computer vision. However, when compared to the extensive literature available for images, the field of videos is still relatively unexplored. On the other hand, the performance of a model in action recognition is heavily affected by domain shift. In this paper, we propose a simple and novel UDA approach for video action recognition. Our approach leverages recent advances on spatio-temporal transformers to build a robust source model that better generalises to the target domain. Furthermore, our architecture learns domain invariant features thanks to the introduction of a novel alignment loss term derived from the Information Bottleneck principle. We report results on two video action recognition benchmarks for UDA, showing state-of-the-art performance on HMDB↔UCF, as well as on Kinetics→NEC-Drone, which is more challenging. This demonstrates the effectiveness of our method in handling different levels of domain shift. The source code is available at https://github.com/vturrisi/UDAVT.