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Deep Learning for Computer Vision · Quiz 1 · May 2025

Deep Learning for Computer Vision · Quiz 1 · May 2025

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Question 155 NAT · 2.0 marks

If we use top-k sampling with k=2 at timestep 1, what is the normalized probability of selecting token “sky” at the timestep=1?

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Question 157 MCQ · 2.0 marks

Source diagram or notation
6406534313841.
104.94
6406534313842.
100.25
6406534313843.
110.56
6406534313844.
98.78

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Question 158 MCQ · 2.0 marks

Which of the following statements is **false**?
6406534313845.
Histogram equalization is a global operation.
6406534313846.
Gaussian filtering is a local operation.
6406534313847.
Convolution in the spatial domain corresponds to multiplication in the frequency domain.
6406534313848.
Median filtering is a global operation.

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Question 159 MCQ · 2.0 marks

Which of the following statements is **True**?
6406534313849.
Gaussian filter is a separable filter because it is linear.
6406534313850.
Median filter is a non-separable filter.
6406534313851.
Gaussian filter is a high-pass filter.
6406534313852.
Mean filter is a non-separable filter because it is non-linear.

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Question 160 MCQ · 2.0 marks

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6406534313856.Source diagram or notation

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Question 161 MCQ · 2.0 marks

Source diagram or notation
6406534313857.
0.9820, 4.00, 1, 4.02, 4.00, 4.00
6406534313858.
0.8808, 3.50, 1, 3.54, 3.50, 3.50
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0.7311, 2.00, 1, 2.13, 2.00, 2.00
6406534313860.
0.5000, 0.00, 0, 0.69, 0.00, 0.00

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Question 162 MCQ · 2.0 marks

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6406534313861.Source diagram or notation
6406534313862.Source diagram or notation
6406534313863.Source diagram or notation
6406534313864.Source diagram or notation

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Question 163 MCQ · 2.0 marks

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6406534313867.Source diagram or notation
6406534313868.Source diagram or notation

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Question 164 MCQ · 2.0 marks

During the double thresholding step in Canny edge detection, the high threshold is set at 100 and the low threshold is 40. A pixel gradient magnitude of 65 will be classified as:
6406534313869.
Strong edge
6406534313870.
Weak edge
6406534313871.
Non-edge
6406534313872.
Ambiguous edge

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Question 165 MCQ · 2.0 marks

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6406534313876.Source diagram or notation

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Question 166 MCQ · 2.0 marks

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6406534313879.Source diagram or notation
6406534313880.Source diagram or notation

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Question 167 MCQ · 2.0 marks

Which of the following statements are True?
6406534313881.
Vanilla Gradient Descent converges faster than Momentum-based GD.
6406534313882.
Momentum based GD oscillates around minima before convergence.
6406534313883.
Noise in Stochastic Gradient descent weight updates – can lead to faster convergence.
6406534313884.
None of these

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Question 168 MCQ · 2.0 marks

Source diagram or notation
6406534313885.
0.16
6406534313886.
0.08
6406534313887.
0.20
6406534313888.
0.12

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Question 169 MCQ · 2.0 marks

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6406534313892.Source diagram or notation

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Question 170 MCQ · 2.0 marks

Source diagram or notation
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6406534313896.Source diagram or notation

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Question 171 MSQ · 3.0 marks

Which of the following statements are **true**?(Select all that apply)
6406534313897.Source diagram or notation
6406534313898.Source diagram or notation
6406534313899.Source diagram or notation
6406534313900.Source diagram or notation

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Question 172 MSQ · 3.0 marks

Which of the following statements are **true** ?(Select all that apply)
6406534313901.Source diagram or notation
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6406534313903.Source diagram or notation
6406534313904.Source diagram or notation

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Question 173 MSQ · 3.0 marks

Consider the following statements. Which of the following statements are true?
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6406534313910.Source diagram or notation

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Question 174 MSQ · 3.0 marks

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6406534313914.Source diagram or notation

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Question 175 MSQ · 3.0 marks

Which of the following statements are **false**?
6406534313915.
Momentum in optimization can cause oscillations around minima when encountering flat regions or saddle points due to the dominance of the momentum term over the small gradient.
6406534313916.
Stochastic Gradient Descent (SGD) with its inherent noise can be beneficial in escaping local minima and saddle points, provided that there is enough gradient information in the neighborhood.
6406534313917.
Adagrad is an optimization algorithm introduced to overcome the diminishing learning rate problem in techniques like RMSProp.
6406534313918.
ADAM is introduced to solve problems in RMSProp by combining RMSProp and Adagrad techniques.

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Question 176 NAT · 3.0 marks

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Question 177 NAT · 3.0 marks

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Question 178 NAT · 3.0 marks

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Question 179 NAT · 3.0 marks

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Question 180 NAT · 3.0 marks

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Question 181 NAT · 3.0 marks

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Question 182 NAT · 3.0 marks

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Question 183 NAT · 3.0 marks

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Question 184 NAT · 3.0 marks

Source diagram or notation

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Question 185 SHORT_TEXT · 1.5 marks

The gradient of an image I points in the direction of the most rapid change in intensity. Taking image derivatives accentuates (a)_________________ frequencies and hence amplifies noise, since the proportion of noise to signal is larger at (b) _________________frequencies. The common solution is to smooth the image prior to computing gradients. Based on the above data, answer the given subquestions.
Enter the correct answer for Blank (a): **NOTE:** Enter the exact answer without any extra space in the beginning or at the end.

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Question 186 SHORT_TEXT · 1.5 marks

The gradient of an image I points in the direction of the most rapid change in intensity. Taking image derivatives accentuates (a)_________________ frequencies and hence amplifies noise, since the proportion of noise to signal is larger at (b) _________________frequencies. The common solution is to smooth the image prior to computing gradients. Based on the above data, answer the given subquestions.
Enter the correct answer for Blank (b): **NOTE:** Enter the exact answer without any extra space in the beginning or at the end.

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Question 187 SHORT_TEXT · 1.5 marks

In Canny edge detection, a large value of the Gaussian kernel spread σ leads to (a)________________ edges, while a small value of σ leads to (b)_________________ edges. Based on the above data, answer the given subquestions.
Enter the correct answer for Blank (a) : **NOTE:** Enter the exact answer without any extra space in the beginning or at the end.

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Question 188 SHORT_TEXT · 1.5 marks

In Canny edge detection, a large value of the Gaussian kernel spread σ leads to (a)________________ edges, while a small value of σ leads to (b)_________________ edges. Based on the above data, answer the given subquestions.
Enter the correct answer for Blank (b) : **NOTE:** Enter the exact answer without any extra space in the beginning or at the end.

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Question 189 NAT · 1.5 marks

You have a visual codebook with 1500 visual words. From a test image, you extract 1200 SIFT descriptors, each of 128 dimensions. After assigning each descriptor to its nearest visual word, you compute the Bag-of-Words histogram by counting descriptor assignments. Based on the above data, answer the given subquestions.
What is the dimensionality of the Bag-of-Words (BoW) feature vector for this image?________________

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Question 190 NAT · 1.5 marks

You have a visual codebook with 1500 visual words. From a test image, you extract 1200 SIFT descriptors, each of 128 dimensions. After assigning each descriptor to its nearest visual word, you compute the Bag-of-Words histogram by counting descriptor assignments. Based on the above data, answer the given subquestions.
If instead, you concatenate all the 1200 descriptors into a single vector (without using BoW), what will be the dimensionality of that vector?__________________

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