Discover why the Vision Transformer (ViT) is the optimal choice for processing image vector embeddings in transformer encoders, surpassing CNNs and other transforms like Hough or Laplace.
Table of Contents
Question
You decide to upgrade from the usual convolutional neural network (CNN) model to a transformer model. Which transformer will you choose to process vector embeddings of an image by a transformer encoder?
A. Vision transformer
B. Codec transformer
C. Hough transformer
D. Laplace transformer
Answer
A. Vision transformer
Explanation
To upgrade from a convolutional neural network (CNN) to a transformer model for processing image vector embeddings, the Vision Transformer (ViT) is the correct choice. Here’s why:
Vision Transformer (ViT) Architecture
ViTs process images by:
- Dividing images into fixed-size patches (e.g., 16×16 pixels), which are flattened and linearly projected into patch embeddings.
- Adding positional embeddings to retain spatial information and a learnable [CLS] token for global image representation.
- Passing these embeddings through a Transformer encoder with multi-head self-attention (MSA) and feedforward layers, enabling global context modeling.
Unlike CNNs, ViTs eliminate inductive biases for local features, instead leveraging self-attention to capture long-range dependencies between patches.
Why Other Options Are Incorrect
Hough Transform: Detects geometric shapes (e.g., lines, circles) via parameter space voting, not transformer-based embeddings.
Laplace Transform: Used for edge detection and image sharpening via mathematical filtering, unrelated to transformer architectures.
Codec Transformer: Not mentioned in standard computer vision literature or search results.
ViTs excel in tasks like image classification, object detection, and segmentation, outperforming CNNs in scenarios requiring global context.
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