Transformer-based Vision Model

A Transformer-based Vision Model is a vision model that is a transformer-based model.

• Counter-Example(s):
  • Transformer-based Language Model.
• See: Pre-Trained Visual Encoder, Language-Vision Multimodal Model.

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References

2023

• GBard
  • A Vision Transformer-based Model is a deep learning model that is inspired by the Transformer architecture, which was originally designed for natural language processing (NLP) tasks.
  • Vision Transformers (ViTs) represent images as a sequence of patches, just like NLP models represent text as a sequence of words. This allows ViTs to learn long-range dependencies between different parts of an image, which is essential for many computer vision tasks, such as image classification and object detection.
  • ViTs have achieved state-of-the-art results on a variety of computer vision benchmarks, and they are quickly becoming the preferred model architecture for many tasks.
  • Here are some examples of Vision Transformer-based Models:
    • DeiT (Data-efficient Image Transformer)
    • ViLT (Vision and Language Transformer)
    • Swin Transformer
    • ConvMixer
    • Pyramid Vision Transformer

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