Guide-yakabatana mibvunzo · Zvakaoma Nhanho

Swin Transformer Quiz

Check shifted-window attention, cross-window communication, hierarchical features, patch merging, and vision-task applications in Swin.

Nzira dzekutungamira dzinoenderanaSwin Transformer
Mubvunzo 1 ye 8

Ko iyo 'Swin' muSwin Transformer inomirirei?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Ko iyo 'Swin' muSwin Transformer inomirirei?
    • Yakachinjwa Windows
    • Hwindo rakapfava
    • Spatial Winding
    • Single Window
  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 anoita sei kuti ruzivo rufambe pakati pemahwindo akasiyana?
    • Inoshandisa zvibatanidza zvinodzokororwa
    • Inobvisa miganhu yehwindo zvachose
    • Inoshandura kupatsanurwa kwehwindo muzvikamu zvinochinjana
    • Inongoshandisa hwindo rimwe chete repasi rose
  4. Chii chinoita chigamba-kubatanidza maseru anoita pakati peSwin nhanho?
    • Wedzera resolution uye bvisa machaneli
    • Donhedza zvigamba
    • Shandura zvimedu kuita mazwi
    • Batanidza zvigamba zvakavakidzana, kuderedza kugadzirisa uye kuwedzera chiteshi
  5. Nei Swin's hierarchical, yakawanda-scale kuburitsa ichinyanya kubatsira?
    • Iyo inovhara zvakanaka mukuonekwa uye zvikamu zvezvikamu zvinotarisira mapiramidhi
    • Inongobatsira chete ne single-label classification
    • Inobvisa kudiwa kwedata rakanyorwa
    • Inoita kuti modhi ive diki pane chero CNN
  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