Attention Mechanisms for Object Recognition with Event-Based Cameras

Event-based cameras are neuromorphic sensors capable of efficiently encoding visual information in the form of sparse sequences of events. Being biologically inspired, they are commonly used to exploit some of the computational and power consumption benefits of biological vision. In this paper we focus on a specific feature of vision: visual attention. We propose two attentive models for event based vision: an algorithm that tracks events activity within the field of view to locate regions of interest and a fully-differentiable attention procedure based on DRAW neural model. We highlight the strengths and weaknesses of the proposed methods on four datasets, the Shifted N-MNIST, Shifted MNIST-DVS, CIFAR10-DVS and N-Caltech101 collections, using the Phased LSTM recognition network as a baseline reference model obtaining improvements in terms of both translation and scale invariance.Comment: WACV2019 camera-ready submissio

Multi-View Stereo with Single-View Semantic Mesh Refinement

While 3D reconstruction is a well-established and widely explored research topic, semantic 3D reconstruction has only recently witnessed an increasing share of attention from the Computer Vision community. Semantic annotations allow in fact to enforce strong class-dependent priors, as planarity for ground and walls, which can be exploited to refine the reconstruction often resulting in non-trivial performance improvements. State-of-the art methods propose volumetric approaches to fuse RGB image data with semantic labels; even if successful, they do not scale well and fail to output high resolution meshes. In this paper we propose a novel method to refine both the geometry and the semantic labeling of a given mesh. We refine the mesh geometry by applying a variational method that optimizes a composite energy made of a state-of-the-art pairwise photo-metric term and a single-view term that models the semantic consistency between the labels of the 3D mesh and those of the segmented images. We also update the semantic labeling through a novel Markov Random Field (MRF) formulation that, together with the classical data and smoothness terms, takes into account class-specific priors estimated directly from the annotated mesh. This is in contrast to state-of-the-art methods that are typically based on handcrafted or learned priors. We are the first, jointly with the very recent and seminal work of [M. Blaha et al arXiv:1706.08336, 2017], to propose the use of semantics inside a mesh refinement framework. Differently from [M. Blaha et al arXiv:1706.08336, 2017], which adopts a more classical pairwise comparison to estimate the flow of the mesh, we apply a single-view comparison between the semantically annotated image and the current 3D mesh labels; this improves the robustness in case of noisy segmentations.Comment: {\pounds}D Reconstruction Meets Semantic, ICCV worksho

ReConvNet: Video Object Segmentation with Spatio-Temporal Features Modulation

We introduce ReConvNet, a recurrent convolutional architecture for semi-supervised video object segmentation that is able to fast adapt its features to focus on any specific object of interest at inference time. Generalization to new objects never observed during training is known to be a hard task for supervised approaches that would need to be retrained. To tackle this problem, we propose a more efficient solution that learns spatio-temporal features self-adapting to the object of interest via conditional affine transformations. This approach is simple, can be trained end-to-end and does not necessarily require extra training steps at inference time. Our method shows competitive results on DAVIS2016 with respect to state-of-the art approaches that use online fine-tuning, and outperforms them on DAVIS2017. ReConvNet shows also promising results on the DAVIS-Challenge 2018 winning the $10$-th position.Comment: CVPR Workshop - DAVIS Challenge 201

Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras

Event-based cameras, also known as neuromorphic cameras, are bioinspired sensors able to perceive changes in the scene at high frequency with low power consumption. Becoming available only very recently, a limited amount of work addresses object detection on these devices. In this paper we propose two neural networks architectures for object detection: YOLE, which integrates the events into surfaces and uses a frame-based model to process them, and fcYOLE, an asynchronous event-based fully convolutional network which uses a novel and general formalization of the convolutional and max pooling layers to exploit the sparsity of camera events. We evaluate the algorithm with different extensions of publicly available datasets and on a novel synthetic dataset.Comment: accepted at CVPR2019 Event-based Vision Worksho

Endothelial Function in Pre-diabetes, Diabetes and Diabetic Cardiomyopathy: A Review

Diabetes mellitus worsens cardiovascular risk profile of affected individuals. Its worldwide increasing prevalence and its negative influences on vascular walls morphology and function are able to induce the expression of several morbidities which worsen the clinical conditions of the patients getting them running towards a reduced survival curve. Although overt diabetes increases the mortality rate of individuals due to its pathogenesis, poor information are in literature about the role of pre-diabetes and family history of diabetes mellitus in the outcome of general population. This emphasizes the importance of early detection of vascular impairment in subjects at risk of developing diabetes. The identification of early stages of atherosclerotic diseases in diabetic persons is a fundamental step in the risk stratification protocols followed-up by physicians in order to have a complete overview about the clinical status of such individuals. Common carotid intima-media thickness, flow-mediated vasodilatation, pulse wave velocity are instrumental tools able to detect the early impairment in cardiovascular system and stratify cardiovascular risk of individuals. The aim of this review is to get a general perspective on the complex relationship between cardiovascular diseases onset, pre-diabetes and family history of diabetes. Furthermore, it points out the influence of diabetes on heart function till the expression of the so-called diabetic cardiomyopathy

ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation

We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed architecture, called ReSeg, is based on the recently introduced ReNet model for image classification. We modify and extend it to perform the more challenging task of semantic segmentation. Each ReNet layer is composed of four RNN that sweep the image horizontally and vertically in both directions, encoding patches or activations, and providing relevant global information. Moreover, ReNet layers are stacked on top of pre-trained convolutional layers, benefiting from generic local features. Upsampling layers follow ReNet layers to recover the original image resolution in the final predictions. The proposed ReSeg architecture is efficient, flexible and suitable for a variety of semantic segmentation tasks. We evaluate ReSeg on several widely-used semantic segmentation datasets: Weizmann Horse, Oxford Flower, and CamVid; achieving state-of-the-art performance. Results show that ReSeg can act as a suitable architecture for semantic segmentation tasks, and may have further applications in other structured prediction problems. The source code and model hyperparameters are available on https://github.com/fvisin/reseg.Comment: In CVPR Deep Vision Workshop, 201

Left ventricular diastolic dysfunction in normotensive postmenopausal women with type 2 diabetes mellitus.

Background The prevalence of heart failure among diabetic patients is high, also in those with normal blood pressure and without coronary artery disease, even when electrocardiogram (ECG) is normal. The goal of our study was to assess the prevalence of left ventricular diastolic dysfunction (LVDD) among diabetic women (DW) and its correlation with glycosylated hemoglobin (HbA1c) levels, obesity status, and ECG parameters. Methods : A group of 456 consecutive normotensive postmenopausal women affected by type 2 diabetes, diagnosed over 5 years, were enrolled. One hundred normotensive non-diabetic postmenopausal women were included as a control group (CG). Rest ECG and trans-thoracic echocardiogram and Doppler were performed. Results : LVDD was present in 103 (23.3%) out of 456 DW, and 8 out of 100 women in CG (8%), p < 0.001. There was no difference in mean age between the two groups: 56 ± 13 and 55 ± 3, respectively (p = 0.3). There were 191 (41.9 %) DW with body mass index (BMI) > 30 kg/m2. Among those, there were 56 (12.3%) with significant prevalence of LVDD, while there were 49 (10.7%) with BMI < 30 kg/m2, p < 0.005. DW with HbA1c > 7.5% comprised a group of 243 (53.3%) patients. Among those, there were 45 (9.9%) with higher prevalence of LVDD, and 15 (3.3%) with HbA1c < 7.5%), p < 0.01. Out of a group of 147 (32.2%) DW with abnormal ECG , 21 had LVDD (4.6%), p = 0,1, and 84 (18.8%) had LVDD with normal ECG. Conclusions: Our data prove a high prevalence of LVDD in asymptomatic diabetic postmenopausal women. This finding is closely related with HbA1c levels and obesity status, not with abnormal ECG, which is a unique cardiologic test recommended by current guidelines in all diabetic patients. We conclude that early detection of high level of HbA1c and obesity (30 kg/m2) may identify women with major risk to develop LVDD. Furthermore, a simple ECG, when normal, is not enough to assess a normal LV diastolic function

Advances in the diagnosis of acute aortic syndromes: Role of imaging techniques.

Aortic diseases include a wide range of pathological conditions: aortic aneurysms, pseudoaneurysms, acute aortic syndromes, atherosclerotic and inflammatory conditions, genetic diseases and congenital anomalies. Acute aortic syndromes have acute onset and may be life-threatening. They include aortic dissection, intramural haematoma, penetrating aortic ulcer and traumatic aortic injury. Pain is the common denominator to all acute aortic syndromes. Pain occurs regardless of age, gender and other associated clinical conditions. In this review, we deal with the main findings in the clinical setting and the most recent indications for diagnostic imaging, which are aimed to start an appropriate treatment and improve the short- and long-term prognosis of these patients. © The Author(s) 2016

Sacubitril/valsartan in COVID-19 patients: the need for trials

We thank Luigi Petramala and Claudio Letizia for their comment1 on our letter about the possible role of sacubitril/valsartan in patients with coronavirus disease 2019 (COVID-19).2 The authors rightly affirm the need for continuing previous therapies with angiotensinconverting enzyme inhibitors (ACE-Is) or sartans in patients with COVID-19, as outlined by recent international consensus papers.3 There is no definite evidence about the harmful or protective use of ACE-Is/sartans in COVID-19 patients.4,5 Dedicated, randomized controlled trials are needed in order to verify the possible worsening of lung infection and/or systemic involvement in patients with COVID-19 who are chronically treated with ACE-Is/sartans. Furthermore, we do not intend to pressurize the indiscriminate change of previous treatments towards sacubitril/valsartan in the absence of evidence from randomized trials. The COVID-19 pandemic forced the scientific community to think about possible, alternative solutions to counteract the multiorgan damage by the virus. We do agree that interrupting specific treatments would increase adverse clinical outcomes in patients, independently from the course of COVID-19, but trying to improve therapeutic solutions is challenging. Sacubitril/ valsartan has already demonstrated superiority over standard therapies in patients suffering from heart failure with reduced ejection fraction (HFrEF), regardless of any comorbidities.6 Moreover, post-hoc analysis from the Comparison of Sacubitril-Valsartan versus Enalapril on Effect on NT-proBNP in Patients Stabilized from an Acute Heart Failure Episode (PIONEER-HF) trial revealed a 42% relative risk reduction in the composite endpoint of death from any cause, re-hospitalization for heart failure, left ventricular assist device implantation, or listing for cardiac transplant, a 42% relative risk reduction in the composite endpoint of cardiovascular death or re-hospitalization for heart failure, and a 39% relative risk reduction in re-hospitalization for heart failure after 8 weeks of treatment with sacubitril/valsartan administered early in patients stabilized during hospitalization for acute decompensated heart failure.7 Furthermore, a significant 50% reduction in NT-proBNP is evident after the first week of treatment with sacubitril/valsartan.8 The need for early administration of sacubitril/valsartan in acute heart failure is probably becoming mandatory in pharmacological management of heart failure patients, although not yet covered by the guidelines. In recent days, the characteristics of cardiac injury during COVID-19 infection have been made available to the medical and scientific community.9,10 In COVID-19 patients, with and without symptoms attributable to pneumonia, there is evidence of a significant increase in NTproBNP, regardless of left ventricular dysfunction. NT-proBNP levels are also the results of acute renal injury and pro-inflammatory molecules such as interleukin-1 and C-reactive protein, which are independent of cardiac function. Shi et al. showed that patients with cardiac injury had a higher rate of mortality during the interval both from symptom onset to admission and from admission to clinical endpoint. Increased death rates were associated with higher levels of NT-proBNP. 9 Gao et al. reported that higher NT-proBNP was an independent risk factor for in-hospital death in patients with severe COVID-19 after adjusting for sex, age, hypertension, coronary heart disease, chronic obstructive pulmonary disease, myoglobin, creatin kinase-MB, high sensitivity troponin-I, white blood cell count, lymphocyte count, C-reactive protein, and procalcitonin.10 Based on the evidence and in relation to the hypotheses generated from our previous correspondence,2 we thought about the possibility of early adoption of sacubitril/valsartan in patients with COVID-19, to maximize the antiinflammatory effects of an enhanced natriuretic peptide system and contain the effects of angiotensin II. Clinical trials in COVID-19 patients are needed in order to validate our hypothesis

Left ventricular diastolic dysfunction in normotensive postmenopausal women with type 2 diabetes mellitus

Background: The prevalence of heart failure among diabetic patients is high, also in those with normal blood pressure and without coronary artery disease, even when electrocardiogram (ECG) is normal. The goal of our study was to assess the prevalence of left ventricular diastolic dysfunction (LVDD) among diabetic women (DW) and its correlation with glycosylated hemoglobin (HbA1c) levels, obesity status, and ECG parameters. Methods: A group of 456 consecutive normotensive postmenopausal women affected by type 2 diabetes, diagnosed over 5 years, were enrolled. One hundred normotensive non-diabetic postmenopausal women were included as a control group (CG). Rest ECG and trans-thoracic echocardiogram and Doppler were performed. Results: LVDD was present in 103 (23.3%) out of 456 DW, and 8 out of 100 women in CG (8%), p < 0.001. There was no difference in mean age between the two groups: 56 ± 13 and 55 ± 3, respectively (p = 0.3). There were 191 (41.9%) DW with body mass index (BMI) > 30 kg/m2. Among those, there were 56 (12.3%) with significant prevalence of LVDD, while there were 49 (10.7%) with BMI < 30 kg/m2, p < 0.005. DW with HbA1c > 7.5% comprised a group of 243 (53.3%) patients. Among those, there were 45 (9.9%) with higher prevalence of LVDD, and 15 (3.3%) with HbA1c < 7.5%, p < 0.01. Out of a group of 147 (32.2%) DW with abnormal ECG , 21 had LVDD (4.6%), p = 0,1, and 84 (18.8%) had LVDD with normal ECG. Conclusions: Our data prove a high prevalence of LVDD in asymptomatic diabetic postmenopausal women. This finding is closely related with HbA1c levels and obesity status, not with abnormal ECG, which is a unique cardiologic test recommended by current guidelines in all diabetic patients. We conclude that early detection of high level of HbA1c and obesity (30 kg/m2) may identify women with major risk to develop LVDD. Furthermore, a simple ECG, when normal, is not enough to assess a normal LV diastolic function.