Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain

Deep neural networks (DNNs) may suffer from significantly degenerated performance when the training and test data are of different underlying distributions. Despite the importance of model generalization to out-of-distribution (OOD) data, the accuracy of state-of-the-art (SOTA) models on OOD data can plummet. Recent work has demonstrated that regular or off-manifold adversarial examples, as a special case of data augmentation, can be used to improve OOD generalization. Inspired by this, we theoretically prove that on-manifold adversarial examples can better benefit OOD generalization. Nevertheless, it is nontrivial to generate on-manifold adversarial examples because the real manifold is generally complex. To address this issue, we proposed a novel method of Augmenting data with Adversarial examples via a Wavelet module (AdvWavAug), an on-manifold adversarial data augmentation technique that is simple to implement. In particular, we project a benign image into a wavelet domain. With the assistance of the sparsity characteristic of wavelet transformation, we can modify an image on the estimated data manifold. We conduct adversarial augmentation based on AdvProp training framework. Extensive experiments on different models and different datasets, including ImageNet and its distorted versions, demonstrate that our method can improve model generalization, especially on OOD data. By integrating AdvWavAug into the training process, we have achieved SOTA results on some recent transformer-based models.Comment: Computer Vision and Image Understanding (CVIU) [under review

Graph Correspondence Transfer for Person Re-identification

In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification. Unlike existing methods, the GCT model formulates person re-identification as an off-line graph matching and on-line correspondence transferring problem. In specific, during training, the GCT model aims to learn off-line a set of correspondence templates from positive training pairs with various pose-pair configurations via patch-wise graph matching. During testing, for each pair of test samples, we select a few training pairs with the most similar pose-pair configurations as references, and transfer the correspondences of these references to test pair for feature distance calculation. The matching score is derived by aggregating distances from different references. For each probe image, the gallery image with the highest matching score is the re-identifying result. Compared to existing algorithms, our GCT can handle spatial misalignment caused by large variations in view angles and human poses owing to the benefits of patch-wise graph matching. Extensive experiments on five benchmarks including VIPeR, Road, PRID450S, 3DPES and CUHK01 evidence the superior performance of GCT model over other state-of-the-art methods.Comment: Accepted to AAAI'18 (Oral). The code is available at http://www.dabi.temple.edu/~hbling/code/gct.ht

Activity-Based Scene Decomposition for Topology Inference of Video Surveillance Network

The topology inference is the study of spatial and temporal relationships among cameras within a video surveillance network. We propose a novel approach to understand activities based on the visual coverage of a video surveillance network. In our approach, an optimal camera placement scheme is firstly presented by using a binary integer programming algorithm in order to maximize the surveillance coverage. Then, each camera view is decomposed into regions based on the Histograms of Color Optical Flow (HCOF), according to the spatial-temporal distribution of activity patterns observed in a training set of video sequences. We conduct experiments by using hours of video sequences captured at an office building with seven camera views, all of which are sparse scenes with complex activities. The results of real scene experiment show that the features of histograms of color optic flow offer important contextual information for spatial and temporal topology inference of a camera network

Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.Comment: 8 pages, CVPR202

GWAS for urinary sodium and potassium excretion highlights pathways shared with cardiovascular traits

© 2019, The Author(s). Urinary sodium and potassium excretion are associated with blood pressure (BP) and cardiovascular disease (CVD). The exact biological link between these traits is yet to be elucidated. Here, we identify 50 loci for sodium and 13 for potassium excretion in a large-scale genome-wide association study (GWAS) on urinary sodium and potassium excretion using data from 446,237 individuals of European descent from the UK Biobank study. We extensively interrogate the results using multiple analyses such as Mendelian randomization, functional assessment, co localization, genetic risk score, and pathway analyses. We identify a shared genetic component between urinary sodium and potassium expression and cardiovascular traits. Ingenuity pathway analysis shows that urinary sodium and potassium excretion loci are over-represented in behavioural response to stimuli. Our study highlights pathways that are shared between urinary sodium and potassium excretion and cardiovascular traits

Pooled analysis of WHO Surgical Safety Checklist use and mortality after emergency laparotomy

Background The World Health Organization (WHO) Surgical Safety Checklist has fostered safe practice for 10 years, yet its place in emergency surgery has not been assessed on a global scale. The aim of this study was to evaluate reported checklist use in emergency settings and examine the relationship with perioperative mortality in patients who had emergency laparotomy. Methods In two multinational cohort studies, adults undergoing emergency laparotomy were compared with those having elective gastrointestinal surgery. Relationships between reported checklist use and mortality were determined using multivariable logistic regression and bootstrapped simulation. Results Of 12 296 patients included from 76 countries, 4843 underwent emergency laparotomy. After adjusting for patient and disease factors, checklist use before emergency laparotomy was more common in countries with a high Human Development Index (HDI) (2455 of 2741, 89.6 per cent) compared with that in countries with a middle (753 of 1242, 60.6 per cent; odds ratio (OR) 0.17, 95 per cent c.i. 0.14 to 0.21, P <0001) or low (363 of 860, 422 per cent; OR 008, 007 to 010, P <0.001) HDI. Checklist use was less common in elective surgery than for emergency laparotomy in high-HDI countries (risk difference -94 (95 per cent c.i. -11.9 to -6.9) per cent; P <0001), but the relationship was reversed in low-HDI countries (+121 (+7.0 to +173) per cent; P <0001). In multivariable models, checklist use was associated with a lower 30-day perioperative mortality (OR 0.60, 0.50 to 073; P <0.001). The greatest absolute benefit was seen for emergency surgery in low- and middle-HDI countries. Conclusion Checklist use in emergency laparotomy was associated with a significantly lower perioperative mortality rate. Checklist use in low-HDI countries was half that in high-HDI countries.Peer reviewe

Réseau de caméras sans fil pour la surveillance et la sécurité des systèmes

In this thesis, we propose a decentralized and collaborative tracking system, based on wireless autonomous camera networks. The distributed mode ensures the robustness of the surveillant system against external attacks or an actual failure of some camera nodes. In fact, the failure of some network components does not compromise the effectiveness of the global decision-making. The proposed technique relies on a variational framework, meeting the communication constraints of the camera network, while ensuring the robustness of the tracking system for a highly maneuvring target. It is worth also noting that the communication between two successive processing cameras is limited to the transmission of Gaussian statistics (a mean and a covariance matrix). In this thesis, we propose also original descriptors robust against the variation of the camera vue, in a non pre-calibrated network. These descriptors are also efficiently implemented in order to meet the real-time constraint during the collaborative tracking processDans le cadre de cette thèse, on propose un système décentralisé et coopératif pour la détection d'intrusion et le suivi d'objets mobiles à l'aide d'un réseau de caméras autonomes, miniatures et sans fil. Ce mode distribué présente l'avantage d'être particulièrement robuste aux attaques extérieures et à la défaillance de caméras puisqu'il est prévu que la perte de composants ne compromette pas l'efficacité du réseau dans son ensemble. La technique proposée repose sur une approche variationnelle s'accommodant des contraintes de communication en termes de débit et de puissance, tout en assurant un traitement robuste par rapport au bruit et au changement brusque de trajectoire. En plus, la communication entre 2 caméras en charge de la mise à jour de la distribution de filtrage se trouve limitée à l'envoi des paramètres d'une seule gaussienne (une moyenne et une covariance). Dans cette thèse, on propose aussi des descripteurs originaux robustes par rapport au changement de vues dans un réseau de caméras non calibrées. Ces descripteurs présentent aussi l avantage de bénéficier d une implémentation rapide, permettant ainsi de respecter la contrainte temps-réel lors du suivi des cibles. Dans cette thèse, on propose aussi des descripteurs originaux robustes par rapport au changement de vues dans un réseau de caméras non calibrées. Ces descripteurs présentent aussi l avantage de bénéficier d une implémentation rapide, permettant ainsi de respecter la contrainte temps-réel lors du suici des ciblesTROYES-SCD-UTT (103872102) / SudocSudocFranceF