27,965 research outputs found

    Adversarially Tuned Scene Generation

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    Generalization performance of trained computer vision systems that use computer graphics (CG) generated data is not yet effective due to the concept of 'domain-shift' between virtual and real data. Although simulated data augmented with a few real world samples has been shown to mitigate domain shift and improve transferability of trained models, guiding or bootstrapping the virtual data generation with the distributions learnt from target real world domain is desired, especially in the fields where annotating even few real images is laborious (such as semantic labeling, and intrinsic images etc.). In order to address this problem in an unsupervised manner, our work combines recent advances in CG (which aims to generate stochastic scene layouts coupled with large collections of 3D object models) and generative adversarial training (which aims train generative models by measuring discrepancy between generated and real data in terms of their separability in the space of a deep discriminatively-trained classifier). Our method uses iterative estimation of the posterior density of prior distributions for a generative graphical model. This is done within a rejection sampling framework. Initially, we assume uniform distributions as priors on the parameters of a scene described by a generative graphical model. As iterations proceed the prior distributions get updated to distributions that are closer to the (unknown) distributions of target data. We demonstrate the utility of adversarially tuned scene generation on two real-world benchmark datasets (CityScapes and CamVid) for traffic scene semantic labeling with a deep convolutional net (DeepLab). We realized performance improvements by 2.28 and 3.14 points (using the IoU metric) between the DeepLab models trained on simulated sets prepared from the scene generation models before and after tuning to CityScapes and CamVid respectively.Comment: 9 pages, accepted at CVPR 201

    Assessments as Teaching and Research Tools in an Environmental Problem-Solving Program for In-Service Teachers

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    This article discusses the use of a scenario-based assessment tool in two environmental geoscience in-service programs for middle school and high school teachers. This tool served both to guide instructional techniques and as a method to evaluate the success of the instructional approach. In each case, participants were assessed before the workshops to reveal misconceptions that could be addressed in program activities and afterwards to reveal shifts in their understanding of concepts and approaches. The researchers noted that this scenario-based assessment was effective in providing guidance in refining instructional techniques and as a method to evaluate the effectiveness of an instructional program. In addition, participating teachers reported significant changes in their teaching as a result of the program. Educational levels: Graduate or professional, Graduate or professional

    Gravitational radiation reaction and inspiral waveforms in the adiabatic limit

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    We describe progress evolving an important limit of binary orbits in general relativity, that of a stellar mass compact object gradually spiraling into a much larger, massive black hole. These systems are of great interest for gravitational wave observations. We have developed tools to compute for the first time the radiated fluxes of energy and angular momentum, as well as instantaneous snapshot waveforms, for generic geodesic orbits. For special classes of orbits, we compute the orbital evolution and waveforms for the complete inspiral by imposing global conservation of energy and angular momentum. For fully generic orbits, inspirals and waveforms can be obtained by augmenting our approach with a prescription for the self force in the adiabatic limit derived by Mino. The resulting waveforms should be sufficiently accurate to be used in future gravitational-wave searches.Comment: Accepted for publication in Phys. Rev. Let

    Computational investigations of maximum flow algorithms

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    "April 1995."Includes bibliographical references (p. 55-57).by Ravindra K. Ahuja ... [et al.
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