Swin 变压器测验

当您参加测验时，就会出现答案和解释。

1. Swin Transformer 中的“Swin”代表什么？
   • 移动窗口
   • 光滑的窗户
   • 空间缠绕
   • 单一窗口
2. What positional information does Swin add within each local attention window?
   • Absolute word embeddings from a text tokenizer
   • A learnable relative-position bias
   • A global convolution kernel shared across all windows
   • A class-specific bounding-box regression target
3. Swin 如何让信息在不同的窗口之间移动？
   • 它使用循环连接
   • 它完全删除窗口边界
   • 它以交替的层数移动窗口分区
   • 它只使用一个全局窗口
4. Swin 阶段之间的补丁合并层有何作用？
   • 提高分辨率并删除通道
   • 随机掉落补丁
   • 将补丁翻译成文字
   • 合并相邻的补丁，降低分辨率并增加通道
5. 为什么 Swin 的分层、多尺度输出特别有用？
   • 它巧妙地插入需要特征金字塔的检测和分割框架中
   • 它仅有助于单标签分类
   • 它消除了对标记数据的需要
   • 它使模型比任何 CNN 都小
6. How does Swin’s attention pattern differ from standard ViT attention?
   • Swin has no attention mechanism
   • Swin computes attention in local windows and shifts partitions between blocks; standard ViT uses global patch attention
   • ViT uses only local convolutions
   • Both always use identical global attention
7. What scaling property does fixed-size window attention provide in Swin?
   • It is independent of image resolution
   • It becomes quadratic in channel count only
   • Attention computation grows linearly with image size for fixed window size
   • It removes all attention computation
8. Why does shifted-window attention use a mask after the cyclic shift?
   • To force every patch to attend to every image patch
   • To remove all positional information
   • To convert window attention into a convolution
   • To prevent tokens from attending across boundaries that should remain separate windows