Learning the Roots of Visual Domain Shift

In this paper we focus on the spatial nature of visual domain shift, attempting to learn where domain adaptation originates in each given image of the source and target set. We borrow concepts and techniques from the CNN visualization literature, and learn domainnes maps able to localize the degree of domain specificity in images. We derive from these maps features related to different domainnes levels, and we show that by considering them as a preprocessing step for a domain adaptation algorithm, the final classification performance is strongly improved. Combined with the whole image representation, these features provide state of the art results on the Office dataset.Comment: Extended Abstrac

Zero-Shot Deep Domain Adaptation

Domain adaptation is an important tool to transfer knowledge about a task (e.g. classification) learned in a source domain to a second, or target domain. Current approaches assume that task-relevant target-domain data is available during training. We demonstrate how to perform domain adaptation when no such task-relevant target-domain data is available. To tackle this issue, we propose zero-shot deep domain adaptation (ZDDA), which uses privileged information from task-irrelevant dual-domain pairs. ZDDA learns a source-domain representation which is not only tailored for the task of interest but also close to the target-domain representation. Therefore, the source-domain task of interest solution (e.g. a classifier for classification tasks) which is jointly trained with the source-domain representation can be applicable to both the source and target representations. Using the MNIST, Fashion-MNIST, NIST, EMNIST, and SUN RGB-D datasets, we show that ZDDA can perform domain adaptation in classification tasks without access to task-relevant target-domain training data. We also extend ZDDA to perform sensor fusion in the SUN RGB-D scene classification task by simulating task-relevant target-domain representations with task-relevant source-domain data. To the best of our knowledge, ZDDA is the first domain adaptation and sensor fusion method which requires no task-relevant target-domain data. The underlying principle is not particular to computer vision data, but should be extensible to other domains.Comment: This paper is accepted to the European Conference on Computer Vision (ECCV), 201

Fatalism, Social Support and Mental Health in Four Former Soviet Cultures

Research on social support has identified differences in levels of support between cultures, but has provided only a limited explanation of the role of values or beliefs in accounting for such variations. In this paper we examine the relationship between fatalism and perceived support amongst 2672 respondents in four former Soviet States (Russia, Georgia, Ukraine and Belorussia), with participants drawn from groups of manual workers, managers, civil servants, students and the retired in these four countries. We also examine the consequences of such social support for mental health across these nations. Findings indicate a small but significant moderator effect for fatalism on the relationship between social support and mental health. These results are discussed in the context of the continuing economic and social challenges facing the citizens of these nations

How transferable are video representations based on synthetic data?

Army Research Office; CCF-2007350 - National Science Foundation; CCF-1955981 - National Science Foundationhttps://openreview.net/pdf?id=lRUCfzs5Hz

Влияние требований эргономики интерьера малотонажного судна на формирование его экстерьера

Саенко, М. Ю. Влияние требований эргономики интерьера малотонажного судна на формирование его экстерьера / М. Ю. Саенко // Гуманіт. вісн. НУК. – Миколаїв : НУК, 2009. – Вип. 2. – С. 6–12.Рассмотрены принципы взаимной размерной координации функционально наиболее важных эргономически и антропометрически обоснованных размерных величин судового интерьера и их логической связи с формированием экстерьера малотоннажного суднаMore important ergonomic and anthropometry ship interior measures quantities. Logical connection with ship exterior

Active Sampling Based on MMD for Model Adaptation

© 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. In this paper, we demonstrate a method for transfer learning with minimal supervised information. Recently, researchers have proposed various algorithms to solve transfer learning problems, especially the unsupervised domain adaptation problem. They mainly focus on how to learn a good common representation and use it directly for downstream task. Unfortunately, they ignore the fact that this representation may not capture target-specific feature for target task well. In order to solve this problem, this paper attempts to capture target-specific feature by utilizing labeled data in target domain. Now it’s a challenge that how to seek as little supervised information as possible to achieve good results. To overcome this challenge, we actively select instances for training and model adaptation based on MMD method. In this process, we try to label some valuable target data to capture target-specific feature and fine-tune the classifier networks. We choose a batch of data in target domain far from common representation space and having maximum entropy. The first requirement is helpful to learn a good representation for target domain and the second requirement tries to improve the classifier performance. Finally, we experiment with our method on several datasets which shows significant improvement and competitive advantage against common methods

On the Effectiveness of Image Rotation for Open Set Domain Adaptation

Open Set Domain Adaptation (OSDA) bridges the domain gap between a labeled source domain and an unlabeled target domain, while also rejecting target classes that are not present in the source. To avoid negative transfer, OSDA can be tackled by first separating the known/unknown target samples and then aligning known target samples with the source data. We propose a novel method to addresses both these problems using the self-supervised task of rotation recognition. Moreover, we assess the performance with a new open set metric that properly balances the contribution of recognizing the known classes and rejecting the unknown samples. Comparative experiments with existing OSDA methods on the standard Office-31 and Office-Home benchmarks show that: (i) our method outperforms its competitors, (ii) reproducibility for this field is a crucial issue to tackle, (iii) our metric provides a reliable tool to allow fair open set evaluation.Comment: accepted at ECCV 202

Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation

Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassing the conventional model training requirement of annotated examples for every category. This is achieved by establishing a mapping connecting low-level features and a semantic description of the label space, referred as visual-semantic mapping, on auxiliary data. Reusing the learned mapping to project target videos into an embedding space thus allows novel-classes to be recognised by nearest neighbour inference. However, existing ZSL methods suffer from auxiliary-target domain shift intrinsically induced by assuming the same mapping for the disjoint auxiliary and target classes. This compromises the generalisation accuracy of ZSL recognition on the target data. In this work, we improve the ability of ZSL to generalise across this domain shift in both model- and data-centric ways by formulating a visual-semantic mapping with better generalisation properties and a dynamic data re-weighting method to prioritise auxiliary data that are relevant to the target classes. Specifically: (1) We introduce a multi-task visual-semantic mapping to improve generalisation by constraining the semantic mapping parameters to lie on a low-dimensional manifold, (2) We explore prioritised data augmentation by expanding the pool of auxiliary data with additional instances weighted by relevance to the target domain. The proposed new model is applied to the challenging zero-shot action recognition problem to demonstrate its advantages over existing ZSL models.Comment: Published in ECCV 201

Delayed baroclinic response of the Antarctic circumpolar current to surface wind stress

Author Posting. © Science in China Press, 2008. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Science in China Series D: Earth Sciences 51 (2008): 1036-1043, doi:10.1007/s11430-008-0074-8.Antarctic Circumpolar Current (ACC) responds to the surface windstress via two processes, i.e., instant barotropic process and delayed baroclinic process. This study focuses on the baroclinic instability mechanism in ACC. That is, the strengthening of surface zonal windstress causes the enhanced tilting of the isopycnal surface, which leads to the intense baroclinic instability. Simultaneously, the mesoscale eddies resulting from the baroclinic instability facilitate the transformation of mean potential energy to eddy energy, which causes the remarkable decrease of the ACC volume transport with the 2-year lag time. This delayed negative correlation between the ACC transport and the zonal windstress may account for the steadiness of the ACC transport in these two decades.Supported by NSCF Outstanding Young Scientist Award (Grant No. 40625017) and the National Basic Research Program of China (Grant No. 2006CB403604). The research was also supported by W. Alan Clark Chair from Woods Hole Oceanographic Institution for RXH and NOAA GLERL contribution No. 1462 for J

Enhanced warming over the global subtropical western boundary currents

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature Climate Change 2 (2012): 161-166, doi:10.1038/nclimate1353.Subtropical western boundary currents are warm, fast flowing currents that form on the western side of ocean basins. They carry warm tropical water to the mid-latitudes and vent large amounts of heat and moisture to the atmosphere along their paths, affecting atmospheric jet streams and mid-latitude storms, as well as ocean carbon uptake. The possibility that these highly energetic and nonlinear currents might change under greenhouse gas forcing has raised significant concerns, but detecting such changes is challenging owing to limited observations. Here, using reconstructed sea surface temperature datasets and newly developed century-long ocean and atmosphere reanalysis products, we find that the post-1900 surface ocean warming rate over the path of these currents is two to three times faster than the global mean surface ocean warming rate. The accelerated warming is associated with a synchronous poleward shift and/or intensification of global subtropical western boundary currents in conjunction with a systematic change in winds over both hemispheres. This enhanced warming may reduce ocean's ability to absorb anthropogenic carbon dioxide over these regions. However, uncertainties in detection and attribution of these warming trends remain, pointing to a need for a long-term monitoring network of the global western boundary currents and their extensions.This work is supported by China National Key Basic Research Project (2007CB411800) and National Natural Science Foundation Projects (40788002, 40921004). WC is supported by the Australian Climate Change Science program and the Southeast Australia Climate Initiative. HN is supported in part by the Japanese Ministry of Education, Culture, Sports, Science and Technology through Grant-in-Aid for Scientific Research on Innovative Areas #2205 and by the Japanese Ministry of Environment through Global Environment Research Fund (S-5). MJM is supported by NOAA’s Climate Program Office.2012-07-2