SwinIR Transformer Restoration Quiz

Answers and explanations appear as you take the quiz.

1. What transformer architecture is SwinIR based on?
   • The Swin Transformer
   • Vision Transformer (ViT) only
   • BERT
   • GPT
2. What is the core attention mechanism in SwinIR?
   • Global all-pixel attention
   • Shifted window-based self-attention
   • Cross-image attention
   • No attention, only convolution
3. What are SwinIR's deep-feature blocks called?
   • Dense Convolution Blocks
   • Inverted Residual Blocks
   • Residual Swin Transformer Blocks (RSTB)
   • Capsule Blocks
4. Why is window-based attention more efficient than global attention here?
   • It uses fewer color channels
   • It skips the reconstruction step
   • It only processes edges
   • Its cost grows linearly with image area instead of quadratically
5. What are the three main stages in SwinIR’s restoration architecture?
   • Shallow feature extraction, deep feature extraction, and image reconstruction
   • Text tokenization, translation, and speech synthesis
   • Noise injection, denoising, and audio decoding
   • Camera sensing, object tracking, and route planning
6. What efficiency advantage did SwinIR demonstrate over CNN competitors?
   • It needed no training data
   • It achieved state-of-the-art results with far fewer parameters
   • It ran without any convolutions
   • It eliminated the need for GPUs
7. What components does a SwinIR Residual Swin Transformer Block combine?
   • A text tokenizer and a language-model decoder
   • A GAN discriminator and a vocoder
   • Swin Transformer layers with residual connections and a convolution
   • A camera sensor and a LiDAR encoder
8. Which image-restoration task besides super-resolution did the SwinIR paper evaluate?
   • Speech recognition
   • Machine translation
   • Protein sequence design
   • JPEG compression-artifact reduction