IV.Writing (20 points)(open-CET4) Directions: Write a composition entitled To Take a Steady Job or a Challenging Job? You should write at least 120 words following the outline given below in Chinese. 1. 有人认为稳定的工作好; 2. 然而其他人则认为有挑战性的工作才有意思; 3. 你的看法是......。
时间: 2024-03-11 21:46:25 浏览: 22
现今社会,人们对于工作的选择存在两种不同的看法。一些人认为稳定的工作是首选,而另一些则认为有挑战性的工作更有意义。
对于稳定的工作,人们会觉得安全感更加强烈。这种工作通常有稳定的收入和福利待遇,而且工作内容相对固定。这种工作可以让人们有更多的时间去关注其他方面的生活,同时也能够提供一定的职业发展空间。
但是,有些人并不喜欢稳定的工作。他们觉得这种工作太过沉闷,缺乏刺激和挑战性。相比之下,有挑战性的工作能够让人们不断超越自我,充满成就感和满足感。这种工作需要不断地学习和探索,可以让人们不断地提高自己的技能和能力,以应对不同的挑战。
在我看来,我更倾向于选择有挑战性的工作。尽管这种工作可能会比较辛苦和累人,但是它能够让我感到生活更加充实和有意义。在追求事业的道路上,我也能够不断地提高自己的能力和技能,实现自己的职业价值。
相关问题
Directions: Write a composition entitled The Importance of Interpersonal Communication. You should write at least 120 words according to the outline given below in Chinese.
人际交流在我们的生活和工作中起着非常重要的作用。以下是人际交流的重要性的几个方面:
首先,人际交流可以帮助我们建立和维护良好的人际关系。通过与他人交流,我们可以更好地了解他们的想法和情感,进而增强彼此之间的信任和理解。
其次,人际交流是解决问题和冲突的关键。通过有效的沟通,我们可以更好地理解不同的观点和思维方式,从而找到解决问题的最佳方法。
最后,人际交流对于个人的职业发展也是至关重要的。在工作场所中,与同事和客户进行良好的交流可以提高个人的工作效率和信誉度。
总之,人际交流在我们的生活和工作中扮演着至关重要的角色。我们应该努力提高自己的交流能力,与他人建立更加良好的关系。
Transformer-Based Visual Segmentation: A Survey
Visual segmentation is one of the most important tasks in computer vision, which involves dividing an image into multiple segments, each of which corresponds to a different object or region of interest in the image. In recent years, transformer-based methods have emerged as a promising approach for visual segmentation, leveraging the self-attention mechanism to capture long-range dependencies in the image.
This survey paper provides a comprehensive overview of transformer-based visual segmentation methods, covering their underlying principles, architecture, training strategies, and applications. The paper starts by introducing the basic concepts of visual segmentation and transformer-based models, followed by a discussion of the key challenges and opportunities in applying transformers to visual segmentation.
The paper then reviews the state-of-the-art transformer-based segmentation methods, including both fully transformer-based approaches and hybrid approaches that combine transformers with other techniques such as convolutional neural networks (CNNs). For each method, the paper provides a detailed description of its architecture and training strategy, as well as its performance on benchmark datasets.
Finally, the paper concludes with a discussion of the future directions of transformer-based visual segmentation, including potential improvements in model design, training methods, and applications. Overall, this survey paper provides a valuable resource for researchers and practitioners interested in the field of transformer-based visual segmentation.