Idanwo ti o sopọ mọ itọsọna · Lile Ipele

Swin Amunawa adanwo

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

Jẹmọ itọsọna awọn ọnaSwin Amunawa
Ibeere 1 ti 8

Kini 'Swin' ni Swin Transformer duro fun?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini 'Swin' ni Swin Transformer duro fun?
    • Windows ti a yipada
    • Ferese Dan
    • Yiyi aaye
    • Ferese Nikan
  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. Bawo ni Swin ṣe jẹ ki alaye gbe laarin awọn ferese lọtọ?
    • O nlo awọn asopọ loorekoore
    • O npa awọn aala window kuro patapata
    • O n yi ipin window pada ni awọn ipele iyipo
    • O nlo ferese agbaye kan ṣoṣo
  4. Kini awọn fẹlẹfẹlẹ patch-parapo ṣe laarin awọn ipele Swin?
    • Mu ipinnu pọ si ki o yọ awọn ikanni kuro
    • Laileto ju awọn abulẹ
    • Tumọ awọn abulẹ si awọn ọrọ
    • Dapọ awọn abulẹ adugbo, idinku ipinnu ati jijẹ awọn ikanni
  5. Kini idi ti ilana ilana Swin, iṣelọpọ iwọn-pupọ wulo paapaa?
    • O pilogi afinju sinu wiwa ati awọn ilana ipin ti o nireti awọn pyramids ẹya
    • O ṣe iranlọwọ nikan pẹlu iyasọtọ aami-ẹyọkan
    • O yọ iwulo fun data ike
    • O jẹ ki awoṣe kere ju eyikeyi 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