Reimagine BiSeNet for Real-Time Domain Adaptation in Semantic Segmentation

Semantic segmentation models have reached remarkable performance across various tasks. However, this performance is achieved with extremely large models, using powerful computational resources and without considering training and inference time. Real-world applications, on the other hand, necessitate models with minimal memory demands, efficient inference speed, and executable with low-resources embedded devices, such as self-driving vehicles. In this paper, we look at the challenge of real-time semantic segmentation across domains, and we train a model to act appropriately on real-world data even though it was trained on a synthetic realm. We employ a new lightweight and shallow discriminator that was specifically created for this purpose. To the best of our knowledge, we are the first to present a real-time adversarial approach for assessing the domain adaption problem in semantic segmentation. We tested our framework in the two standard protocol: GTA5 to Cityscapes and SYNTHIA to Cityscapes. Code is available at: https://github.com/taveraantonio/RTDA.Comment: Accepted at I-RIM 3D 202

IDDA: a large-scale multi-domain dataset for autonomous driving

Semantic segmentation is key in autonomous driving. Using deep visual learning architectures is not trivial in this context, because of the challenges in creating suitable large scale annotated datasets. This issue has been traditionally circumvented through the use of synthetic datasets, that have become a popular resource in this field. They have been released with the need to develop semantic segmentation algorithms able to close the visual domain shift between the training and test data. Although exacerbated by the use of artificial data, the problem is extremely relevant in this field even when training on real data. Indeed, weather conditions, viewpoint changes and variations in the city appearances can vary considerably from car to car, and even at test time for a single, specific vehicle. How to deal with domain adaptation in semantic segmentation, and how to leverage effectively several different data distributions (source domains) are important research questions in this field. To support work in this direction, this paper contributes a new large scale, synthetic dataset for semantic segmentation with more than 100 different source visual domains. The dataset has been created to explicitly address the challenges of domain shift between training and test data in various weather and view point conditions, in seven different city types. Extensive benchmark experiments assess the dataset, showcasing open challenges for the current state of the art. The dataset will be available at: https://idda-dataset.github.io/home/ .Comment: Accepted at IROS 2020 and RA-L. Download at: https://idda-dataset.github.io/home

Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images

In this paper, we investigate the problem of Semantic Segmentation for agricultural aerial imagery. We observe that the existing methods used for this task are designed without considering two characteristics of the aerial data: (i) the top-down perspective implies that the model cannot rely on a fixed semantic structure of the scene, because the same scene may be experienced with different rotations of the sensor; (ii) there can be a strong imbalance in the distribution of semantic classes because the relevant objects of the scene may appear at extremely different scales (e.g., a field of crops and a small vehicle). We propose a solution to these problems based on two ideas: (i) we use together a set of suitable augmentation and a consistency loss to guide the model to learn semantic representations that are invariant to the photometric and geometric shifts typical of the top-down perspective (Augmentation Invariance); (ii) we use a sampling method (Adaptive Sampling) that selects the training images based on a measure of pixel-wise distribution of classes and actual network confidence. With an extensive set of experiments conducted on the Agriculture-Vision dataset, we demonstrate that our proposed strategies improve the performance of the current state-of-the-art method.Comment: CVPR 2022 Workshop - Agriculture Visio

OPS4Math project - Optimization and Problem Solving for Teaching of Mathematics: teaching strategy, organization and objectives

[EN] Several initiatives have been implemented worldwide to foster student interest towards STEM disciplines. These initiatives are based on the awareness that mathematics is essential for scientific and technological advancement: it trains to reasoning and reflection, stimulates logical capabilities and intuition, improve investigation attitude. Most of them recognize also that mathematical problem solving represents an effective way to support teachers and students in their teaching and learning activities, respectively. In this context, this work is aimed at presenting OPS4Math (Optimization and Problem Solving for Teaching of Mathematics), a training project for Secondary School teachers, supported by Italian Ministry of University and Research. The driving idea, widely discussed by the scientific community, is to operate a reversal of the didactical perspective: starting from phenomena/problems to introduce concepts of data, variables, relationships and functions in an appealing way. We present project organization, structure and aims, to give useful hints for its replication.Boccia, M.; Masone, A.; Orabona, A.; Sforza, A.; Sterle, C. (2022). OPS4Math project - Optimization and Problem Solving for Teaching of Mathematics: teaching strategy, organization and objectives. En 8th International Conference on Higher Education Advances (HEAd'22). Editorial Universitat Politècnica de València. 549-557. https://doi.org/10.4995/HEAd22.2022.1463054955

A Borehole Muon Telescope for Underground Muography

Radiographic imaging with muons by absorption, also called Muon Radiography or Muography, is a methodology based on the characteristic of the matter to be crossed by high energy muons. This physical property allows muons to pass through the material with a measurable degree of absorption depending on the density of the material. Muon Radiography applies to several different situations and is particularly suitable for investigating subsoil of civil or archaeological interest. This kind of applications needs the muon detector to be installed below the target region. A novel borehole cylindrical detector has been built and tested for use in harsh conditions and for limited space installations. It is based on the past expertise with scintillator detectors and is composed of two types of scintillating elements, bar-shaped and arcshaped. Due to its size, it can be easily installed in drilled holes of 25 cm in diameter or more, typically economical to make. Here, we describe the idea, commissioning, and some preliminary results

New techniques versus standard mapping for sentinel lymph node biopsy in breast cancer: a systematic review and meta-analysis

: New tracers for sentinel lymph node biopsy (SLNB), as indocyanine green (ICG), superparamagnetic iron oxide (SPIO) and micro bubbles, have been recently introduced in clinical practice showing promising but variable results. We reviewed the available evidence comparing these new techniques with the standard tracers to evaluate their safety. To identify all available studies, a systematic search was performed in all electronic databases. Data regarding sample size, mean number of SLN harvested for patient, number of metastatic SLN and SLN identification rate of all studies were extracted. No significant differences were found in terms of SLNs identification rates between SPIO, RI and BD but with a higher identification rate with the use of ICG. No significant differences were also found for the number of metastatic lymph nodes identified between SPIO, RI and BD and the mean number of SLNs identified between SPIO and ICG versus conventional tracers. A statistically significant differences in favor of ICG was reported for the comparison between ICG and conventional tracers for the number of metastatic lymph nodes identified. Our meta-analysis demonstrates that the use of both ICG and SPIO for the pre-operative mapping of sentinel lymph nodes in breast cancer treatment is adequately effective

Induction of a transmissible tau pathology by traumatic brain injury.

Traumatic brain injury is a risk factor for subsequent neurodegenerative disease, including chronic traumatic encephalopathy, a tauopathy mostly associated with repetitive concussion and blast, but not well recognized as a consequence of severe traumatic brain injury. Here we show that a single severe brain trauma is associated with the emergence of widespread hyperphosphorylated tau pathology in a proportion of humans surviving late after injury. In parallel experimental studies, in a model of severe traumatic brain injury in wild-type mice, we found progressive and widespread tau pathology, replicating the findings in humans. Brain homogenates from these mice, when inoculated into the hippocampus and overlying cerebral cortex of naïve mice, induced widespread tau pathology, synaptic loss, and persistent memory deficits. These data provide evidence that experimental brain trauma induces a self-propagating tau pathology, which can be transmitted between mice, and call for future studies aimed at investigating the potential transmissibility of trauma associated tau pathology in humans