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