Kuis terkait panduan · Sedang Tingkat
Kuis Transformator Visi
Jelajahi token patch ViT dan perhatiannya, dengan temuan penelitian asli, informasi posisi, jumlah patch, dan pilihan status klasifikasi.
Pertanyaan 1 dari 8
How does the original plain ViT convert an image into input tokens for its encoder?
Pratinjau setiap pertanyaan8 pertanyaan
Jawaban dan penjelasan muncul saat Anda mengikuti kuis.
- How does the original plain ViT convert an image into input tokens for its encoder?
- It must read one raw pixel per recurrent step
- It linearly embeds flattened non-overlapping patches
- It converts the whole image into a mandatory single scalar
- It removes all learned input projections
- Which mechanism supports direct interactions between allowed patch tokens in the original dense ViT encoder?
- Perhatian pada diri sendiri
- Only a fixed local max-pooling operation
- Only alphabetical ordering of patch labels
- Only a random image crop
- Which architectural difference did the original ViT study discuss relative to convolutional networks?
- A guarantee that it contains fewer parameters than every CNN
- An inability to process color images
- A guarantee that every vision task needs the same huge dataset
- Fewer convolution-style locality and translation-equivariance biases
- Apa tujuan pengkodean posisi dalam Vision Transformer?
- Untuk mengompres ukuran file gambar
- Untuk mengacak tambalan secara acak untuk augmentasi
- Untuk memberi tahu model di mana setiap patch berada pada gambar aslinya
- Untuk mengubah gambar berwarna menjadi skala abu-abu
- Kelemahan praktis dari self-attention terhadap banyak patch gambar adalah biayanya yang semakin besar:
- Kuadrat dengan jumlah patch
- Linear dengan jumlah patch
- Tidak sama sekali dengan ukuran gambar
- Hanya dengan jumlah saluran warna
- What conclusion is supported by the original ViT classification experiments under their stated pretraining and transfer conditions?
- Every transformer must outperform every CNN on every dataset
- A pure patch transformer could perform competitively with compared CNNs
- All convolutional operations are invalid for image recognition
- No training data is needed for image classification
- For a 224-by-224 image divided into non-overlapping 16-by-16 patches, how many patch tokens are there before adding a class token?
- 197
- 224
- 196
- 256
- In the original ViT classification design, which encoder state supplies the classification head?
- Only an unembedded raw input pixel
- A required random token not processed by the encoder
- Only the first patch’s original filename
- The final state of the learned class token