1,921 research outputs found
Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models
Given large amount of real photos for training, Convolutional neural network
shows excellent performance on object recognition tasks. However, the process
of collecting data is so tedious and the background are also limited which
makes it hard to establish a perfect database. In this paper, our generative
model trained with synthetic images rendered from 3D models reduces the
workload of data collection and limitation of conditions. Our structure is
composed of two sub-networks: semantic foreground object reconstruction network
based on Bayesian inference and classification network based on multi-triplet
cost function for avoiding over-fitting problem on monotone surface and fully
utilizing pose information by establishing sphere-like distribution of
descriptors in each category which is helpful for recognition on regular photos
according to poses, lighting condition, background and category information of
rendered images. Firstly, our conjugate structure called generative model with
metric learning utilizing additional foreground object channels generated from
Bayesian rendering as the joint of two sub-networks. Multi-triplet cost
function based on poses for object recognition are used for metric learning
which makes it possible training a category classifier purely based on
synthetic data. Secondly, we design a coordinate training strategy with the
help of adaptive noises acting as corruption on input images to help both
sub-networks benefit from each other and avoid inharmonious parameter tuning
due to different convergence speed of two sub-networks. Our structure achieves
the state of the art accuracy of over 50\% on ShapeNet database with data
migration obstacle from synthetic images to real photos. This pipeline makes it
applicable to do recognition on real images only based on 3D models.Comment: 14 page
Standardization of Translation of Rail Transit Public Signs in the Greater Capital Area of Chinese Mainland
Based on an empirical study, the present research finds that the translation of public signs of rail transit systems in Beijing and Tianjin lacks unified standard and criteria, which will be incompatible with the international status of the region. This paper aims at providing a unified translation criterion for the rail transit public signs in light of the theory of intertextuality
On-demand Curriculum Rebooting for BA Programs at the School of Interpreting and Translation of BISU in the Post-Covid World
The SARS-Cov-2 pandemic has greatly re-shaped the world in almost all aspects such as economy, society, education, as well as international affairs. Holding high the great banner of Chinese socialism and Xi Jinping Thought, the current research attempts to describe the adjustments of teaching patterns and curriculum for translation majors at Beijing International Studies University in the post-Covid period
Assessment of the Translation and Post-Editing of Machine Translation (MT) With Special Reference to Chinese-English Translation
The current research reports the real performance of machine translation engines (DeepL and GPT-3.5) in translating Classical Chinese into Modern English as well as the post-editing quality of GPT-3.5. The statistical data reveals that: 1) machine translation saves more time and processing energy than human translators; 2) GPT-3.5’s performance in Chinese-English translation is better than Deepl, and it has the advantage of post-editing and self-evolution; 3) Human translators’ ability of semantic processing is superior than DeepL and GPT-3.5. Thus human translators and machine translation engines shall have a good cooperation in improving the accuracy, comprehensibility and fluency of translated texts
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