973 research outputs found
Scene Understanding for Autonomous Manipulation with Deep Learning
Over the past few years, deep learning techniques have achieved tremendous success
in many visual understanding tasks such as object detection, image segmentation,
and caption generation. Despite this thriving in computer vision and natural language
processing, deep learning has not yet shown signicant impact in robotics.
Due to the gap between theory and application, there are many challenges when
applying the results of deep learning to the real robotic systems. In this study,
our long-term goal is to bridge the gap between computer vision and robotics by
developing visual methods that can be used in real robots. In particular, this work
tackles two fundamental visual problems for autonomous robotic manipulation: affordance
detection and ne-grained action understanding. Theoretically, we propose
dierent deep architectures to further improves the state of the art in each problem.
Empirically, we show that the outcomes of our proposed methods can be applied in
real robots and allow them to perform useful manipulation tasks
A see-saw scenario of an flavour symmetric standard model
A see-saw scenario for an flavour symmetric standard model is
presented. The latter, compared with the standard model, has an extended field
content adopting now an additional symmetry structure (along with the
standard model symmetry). As before, the see-saw mechanism can be realized in
several models of different types depending on different ways of neutrino mass
generation corresponding to the introduction of new (heavy in general) fields
with different symmetry structures. In the present paper, a general description
of all these see-saw types is made with a more detailed investigation on type-I
models, while for type-II and type-III models a similar strategy can be
followed. As within the original see-saw mechanism, the symmetry structure of
the standard model fields decides the number and the symmetry structure of the
new fields. In a model considered here, the scalar sector consists of three
standard-model-Higgs-like iso-doublets (-doublets) forming together an
-triplet, and three iso-singlets transforming as three singlets (1,
and ) of . In the lepton sector, the three left-handed lepton
iso-doublets form an -triplet, while the three right-handed charged
leptons are either -singlets in one version of the model, or components of
an -triplet in another version. To generate neutrino masses through, say,
the type-I see-saw mechanism, it is natural to add four right-handed neutrino
multiplets, including one -triplet and three -singlets. For an
interpretation, the model is applied to deriving some physics quantities such
as neutrinoless double beta decay effective mass , CP
violation phase and Jarlskog parameter , which can be
verified experimentally.Comment: LaTeX, 31 pages, 12 figures, 6 tables. V3: some parts modifie
Has the U.S.-Vietnam Bilateral Trade Agreement Led to Higher FDI into Vietnam?
In December 2001, a Bilateral Trade Agreement (BTA) came into effect that normalized economic relations between the United States and Vietnam. The resulting surge in trade surpassed most expectations. The impact of the BTA on FDI, however, has been less visible, especially with regard to U.S. FDI into Vietnam. This paper uses new data that accounts for FDI by U.S. subsidiaries resident in third counties to show that U.S. firms have been much more aggressive investors in Vietnam than normally reported in typical bilateral FDI data using Balance of Payments definitions of capital flows. While the U.S. is widely reported as the 11th largest investor into Vietnam, the new data shows that U.S.-related FDI exceeded all other countries in 2004. Although a formal model is not developed, descriptive data supports strongly the conclusion that the BTA has had a major impact on FDI into Vietnam, especially with regard to FDI from U.S. multinationals.FDI; Trade Agreement
Innovation and Export of Vietnam’s SME Sector
Innovation has long been considered an important factor for creating and maintaining the competitiveness of nations and firms. The relationship between innovation and exporting has been investigated for many countries. However, there is a paucity of research in Vietnam with respect to this issue. In this paper we examine whether innovation performed by Vietnam’s small and medium enterprises (SMEs) enhances their exporting likelihood. Using the recently released Vietnam Small and Medium Enterprise Survey 2005, we find that innovation as measured directly by ‘new products’, ‘new production process’ and ‘improvement of existing products’ are important determinants of exports by Vietnamese SMEs.Vietnam; Export; Innovation; Small and Medium Enterprise
Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic Segmentation
Preparing training data for deep vision models is a labor-intensive task. To
address this, generative models have emerged as an effective solution for
generating synthetic data. While current generative models produce image-level
category labels, we propose a novel method for generating pixel-level semantic
segmentation labels using the text-to-image generative model Stable Diffusion
(SD). By utilizing the text prompts, cross-attention, and self-attention of SD,
we introduce three new techniques: class-prompt appending, class-prompt
cross-attention, and self-attention exponentiation. These techniques enable us
to generate segmentation maps corresponding to synthetic images. These maps
serve as pseudo-labels for training semantic segmenters, eliminating the need
for labor-intensive pixel-wise annotation. To account for the imperfections in
our pseudo-labels, we incorporate uncertainty regions into the segmentation,
allowing us to disregard loss from those regions. We conduct evaluations on two
datasets, PASCAL VOC and MSCOCO, and our approach significantly outperforms
concurrent work. Our benchmarks and code will be released at
https://github.com/VinAIResearch/Dataset-DiffusionComment: Accepted to NeurIPS 2023. Our project page:
https://dataset-diffusion.github.io
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