引导式测验 · 中等 等级

视觉变形金刚测验

通过原始研究结果、位置信息、补丁计数和分类状态选择，探索 ViT 的补丁标记和注意力。

相关引导路径视觉变形金刚
问题 1 的 8

原始的普通 ViT 如何将图像转换为其编码器的输入标记？

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当您参加测验时，就会出现答案和解释。

  1. 原始的普通 ViT 如何将图像转换为其编码器的输入标记？
    • It must read one raw pixel per recurrent step
    • It linearly embeds flattened non-overlapping patches
    • It converts the whole image into a mandatory single scalar
    • It removes all learned input projections
  2. 哪种机制支持原始密集 ViT 编码器中允许的补丁令牌之间的直接交互？
    • 自我关注
    • Only a fixed local max-pooling operation
    • Only alphabetical ordering of patch labels
    • Only a random image crop
  3. 最初的 ViT 研究讨论了相对于卷积网络的哪些架构差异？
    • A guarantee that it contains fewer parameters than every CNN
    • An inability to process color images
    • A guarantee that every vision task needs the same huge dataset
    • Fewer convolution-style locality and translation-equivariance biases
  4. Vision Transformer 中位置编码的目的是什么？
    • 压缩图像文件大小
    • 随机打乱补丁以进行增强
    • 告诉模型每个补丁在原始图像中的位置
    • 将彩色图像转换为灰度图像
  5. 对许多图像块进行自注意力的实际缺点是其成本会增加：
    • 与补丁数量成二次方
    • 与补丁数量成线性关系
    • 与图像大小完全无关
    • 仅与颜色通道数有关
  6. 原始 ViT 分类实验在规定的预训练和转移条件下支持什么结论？
    • Every transformer must outperform every CNN on every dataset
    • A pure patch transformer could perform competitively with compared CNNs
    • All convolutional operations are invalid for image recognition
    • No training data is needed for image classification
  7. For a 224-by-224 image divided into non-overlapping 16-by-16 patches, how many patch tokens are there before adding a class token?
    • 197
    • 224
    • 196
    • 256
  8. In the original ViT classification design, which encoder state supplies the classification head?
    • Only an unembedded raw input pixel
    • A required random token not processed by the encoder
    • Only the first patch’s original filename
    • The final state of the learned class token