FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion

Sensor fusion can significantly improve the performance of many computer vision tasks. However, traditional fusion approaches are either not data-driven and cannot exploit prior knowledge nor find regularities in a given dataset or they are restricted to a single application. We overcome this shortcoming by presenting a novel deep hierarchical variational autoencoder called FusionVAE that can serve as a basis for many fusion tasks. Our approach is able to generate diverse image samples that are conditioned on multiple noisy, occluded, or only partially visible input images. We derive and optimize a variational lower bound for the conditional log-likelihood of FusionVAE. In order to assess the fusion capabilities of our model thoroughly, we created three novel datasets for image fusion based on popular computer vision datasets. In our experiments, we show that FusionVAE learns a representation of aggregated information that is relevant to fusion tasks. The results demonstrate that our approach outperforms traditional methods significantly. Furthermore, we present the advantages and disadvantages of different design choices.Comment: Accepted at ECCV 202

SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects

To enable meaningful robotic manipulation of objects in the real-world, 6D pose estimation is one of the critical aspects. Most existing approaches have difficulties to extend predictions to scenarios where novel object instances are continuously introduced, especially with heavy occlusions. In this work, we propose a few-shot pose estimation (FSPE) approach called SA6D, which uses a self-adaptive segmentation module to identify the novel target object and construct a point cloud model of the target object using only a small number of cluttered reference images. Unlike existing methods, SA6D does not require object-centric reference images or any additional object information, making it a more generalizable and scalable solution across categories. We evaluate SA6D on real-world tabletop object datasets and demonstrate that SA6D outperforms existing FSPE methods, particularly in cluttered scenes with occlusions, while requiring fewer reference images

What Matters for Meta-Learning Vision Regression Tasks?

Meta-learning is widely used in few-shot classification and function regression due to its ability to quickly adapt to unseen tasks. However, it has not yet been well explored on regression tasks with high dimensional inputs such as images. This paper makes two main contributions that help understand this barely explored area. \emph{First}, we design two new types of cross-category level vision regression tasks, namely object discovery and pose estimation of unprecedented complexity in the meta-learning domain for computer vision. To this end, we (i) exhaustively evaluate common meta-learning techniques on these tasks, and (ii) quantitatively analyze the effect of various deep learning techniques commonly used in recent meta-learning algorithms in order to strengthen the generalization capability: data augmentation, domain randomization, task augmentation and meta-regularization. Finally, we (iii) provide some insights and practical recommendations for training meta-learning algorithms on vision regression tasks. \emph{Second}, we propose the addition of functional contrastive learning (FCL) over the task representations in Conditional Neural Processes (CNPs) and train in an end-to-end fashion. The experimental results show that the results of prior work are misleading as a consequence of a poor choice of the loss function as well as too small meta-training sets. Specifically, we find that CNPs outperform MAML on most tasks without fine-tuning. Furthermore, we observe that naive task augmentation without a tailored design results in underfitting.Comment: Accepted at CVPR 202

What Matters For Meta-Learning Vision Regression Tasks?

Meta-learning is widely used in few-shot classification and function regression due to its ability to quickly adapt to unseen tasks. However, it has not yet been well explored on regression tasks with high dimensional inputs such as images. This paper makes two main contributions that help understand this barely explored area. First, we design two new types of cross-category level vision regression tasks, namely object discovery and pose estimation of unprecedented complexity in the meta-learning domain for computer vision. To this end, we (i) exhaustively evaluate common meta-learning techniques on these tasks, and (ii) quantitatively analyze the effect of various deep learning techniques commonly used in recent meta-learning algorithms in order to strengthen the generalization capability: data augmentation, domain randomization, task augmentation and meta-regularization. Finally, we (iii) provide some insights and practical recommendations for training meta-learning algorithms on vision regression tasks. Second, we propose the addition of functional contrastive learning (FCL) over the task representations in Conditional Neural Processes (CNPs) and train in an end-to-end fashion. The experimental results show that the results of prior work are misleading as a consequence of a poor choice of the loss function as well as too small meta-training sets. Specifically, we find that CNPs outperform MAML on most tasks without fine-tuning. Furthermore, we observe that naive task augmentation without a tailored design results in underfitting

Mucositis after Reduced Intensity Conditioning and Allogeneic Stem Cell Transplantation

Background: Therapyrelated mucositis is associated with considerable morbidity. This complication following allogeneic stem cell therapy (alloSCT) is less severe after reduced intense conditioning (RIC); however, even here it may be serious. Methods: 52 patients (male: n = 35 (67%), female: n = 17 (33%)) at a median age of 62 years (35–73 years) underwent alloSCT after RIC. Conditioning was either total body irradiation (TBI)2Gy/±fludarabine (n = 33, 63.5%) or chemotherapy based. Graftversushost disease (GvHD) prophylaxis was carried out with cyclosporine A ± mycophenolate mofetil (MMF). 45 patients (87%) received shortcourse methotrexate (MTX). Mucositis was graded according to the Bearman and the World Health Organisation (WHO) scale. A variety of parameters were correlated with mucositis. Results: The Bearman and WHO scales showed excellent correlation. Mucositis was significantly more severe after chemotherapybased conditioning compared to conditioning with TBI2Gy/±fludarabine (p < 0.002) as well as in cases with an increase in creatinine levels above the upper normal value (UNV) on day +1 after SCT (p < 0.05). Furthermore, the severity correlated with time to engraftment of leucocytes (correlation coefficient (cc) = 0.26, p < 0.02) and thrombocytes (cc = 0.38, p < 0.001). Conclusions: The conditioning regimen and increased creatinine levels at day +1 were identified as factors predicting the severity of mucositis after RICSCT. Creatinine levels on day +1 after SCT may help identify patients at risk for severe mucositis in the further course of transplantation

Examining the incorporation of small-scale recurring disasters in emergency management frameworks: Insights from Aotearoa-New Zealand

Risks pertaining to small-scale recurring disasters are generally not considered by emergency management policies. While their impacts are not immediately recognisable, their recurrent manifestation may result in cumulative as well as indirect impacts. Yet, small-scale recurring disasters both remain under-studied in disaster studies and are often not incorporated in disaster planning and policy. This paper contributes to filling this gap in knowledge by investigating the extent to which the emergency management framework of Aotearoa-New Zealand addresses small-scale recurring disasters through a targeted analysis of high-order policy documents. The findings confirm the incomplete reflection of risk identification related to small-scale recurring disasters in the documents analyzed. The paper reaffirms that small-scale recurring disasters should be more explicitly integrated in disaster management policy regimes to eliminate the differences at the lower administrative levels of risk treatment. It also argues for the re-evaluation of short-term solutions (such as insurance coverage) that only improve recovery outcomes temporarily, and the consideration of long-term risk reduction policies for achieving more sustainable recovery outcomes

Prenatal dexamethasone treatment for classic 21-hydroxylase deficiency in Europe

Dexamethasone; PrenatalDexametasona; PrenatalDexametasona; PrenatalObjective To assess the current medical practice in Europe regarding prenatal dexamethasone (Pdex) treatment of congenital adrenal hyperplasia (CAH) due to 21-hydroxylase deficiency. Design and methods A questionnaire was designed and distributed, including 17 questions collecting quantitative and qualitative data. Thirty-six medical centres from 14 European countries responded and 30 out of 36 centres were reference centres of the European Reference Network on Rare Endocrine Conditions, EndoERN. Results Pdex treatment is currently provided by 36% of the surveyed centres. The treatment is initiated by different specialties, that is paediatricians, endocrinologists, gynaecologists or geneticists. Regarding the starting point of Pdex, 23% stated to initiate therapy at 4–5 weeks postconception (wpc), 31% at 6 wpc and 46 % as early as pregnancy is confirmed and before 7 wpc at the latest. A dose of 20 µg/kg/day is used. Dose distribution among the centres varies from once to thrice daily. Prenatal diagnostics for treated cases are conducted in 72% of the responding centres. Cases treated per country and year vary between 0.5 and 8.25. Registries for long-term follow-up are only available at 46% of the centres that are using Pdex treatment. National registries are only available in Sweden and France. Conclusions This study reveals a high international variability and discrepancy in the use of Pdex treatment across Europe. It highlights the importance of a European cooperation initiative for a joint international prospective trial to establish evidence-based guidelines on prenatal diagnostics, treatment and follow-up of pregnancies at risk for CAH.This work was supported by the Deutsche Forschungsgemeinschaft (Heisenberg Professorship, 325768017 to N R and 314061271-TRR205 to N R and A H), the European Commission for funding EndoERN CHAFEA FPA grant no. 739527, the Eva Luise und Horst Köhler Stiftung & Else Kröner-Fresenius-Stiftung (2019_KollegSE.03 to H N) and the Stockholm County Council (Senior clinical research fellowship dnr RS 2019-1140 to S L), Stiftelsen Frimurare Barnhuset i Stockholm and Lisa and Johan Grönbergs Stiftelse