Kediska Transformer Swin

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Muxuu u taagan yahay 'Swin' ee ku jira beddelka Swin?
   • Daaqadaha la bedelay
   • Daaqad siman
   • Dabayl Baaxadda
   • Daaqad kali ah
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. Sidee Swin ugu ogolaataa macluumaadka u dhexeeya daaqadaha kala duwan?
   • Waxay isticmaashaa isku xirka soo noqnoqda
   • Waxay tirtirtaa gebi ahaanba xuduudaha daaqadaha
   • Waxay u beddeshaa qaybta daaqadda ee lakabyo beddelan
   • Waxa ay isticmaasha hal daaqad oo caalami ah
4. Maxay sameeyaan lakabyada isku-dhafka ah ee u dhexeeya marxaladaha Swin?
   • Kordhi xallinta oo ka saar kanaalada
   • Si aan kala sooc lahayn u tuur balastarrada
   • U tarjun balastar ereyo
   • Isku dar balasmaha deriska ah, yaraynta xallinta iyo kordhinta kanaalada
5. Waa maxay sababta kala sarraynta, wax-soo-saarka cabbirka badan ee Swin gaar ahaan waxtar u leeyahay?
   • Waxay si habsami leh ugu xirtaa ogaanshaha iyo qaab-dhismeedka kala qaybinta kuwaas oo filanaya ahraamyada sifada
   • Waxa kaliya oo ay caawisaa kala soocida hal-calaamadaha
   • Waxay meesha ka saaraysaa baahida xogta calaamadeysan
   • Waxay ka dhigaysa moodelka mid ka yar CNN kasta
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