Question 81 MSQ · 2.0 marks
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Deep Learning for Computer Vision · Quiz 1 · May 2024
Deep Learning for Computer Vision · Quiz 1 · May 2024
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Question 82 MSQ · 2.0 marks
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Question 83 MSQ · 2.0 marks
Which of the following are **true**? (Select all that apply)
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Question 84 MCQ · 2.0 marks
Which of the following statements is True?
Gaussian filter is Separable filter because it is non linear.
Median filter is a non separable filter
Gaussian filter is a High Pass filter.
Mean filter is a non separable filter because it is non linear
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Question 85 MCQ · 2.0 marks
In Bilateral filtering, we do not reject the pixels whose values differ too much from the central
pixel value.
TRUE
FALSE
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Question 86 MCQ · 2.0 marks
Which of the following is the Fourier image of the given intensity image?
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Question 87 MCQ · 2.0 marks
Identify the correct sequence of steps in Scale Invariant Feature Transform (SIFT) method. Steps
listed below:
1. Keypoint Descriptor
2. Keypoint Localization
3. Scale-space Extrema Detection
4. Orientation Estimation
4→ 2→ 1→ 3
3→ 2→ 4→ 1
3→ 1→ 2→ 4
None of these
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Question 88 MCQ · 2.0 marks
1→iii, 2 → ii , 3→ i , 4 → iv
1→iv, 2→ i , 3 → ii , 4→ iii
1→ii, 2 → iii , 3→ iv , 4→ i
None of these
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Question 89 MCQ · 2.0 marks
1→ iii, 2→ i, 3 → ii
1→ iii, 2→ ii, 3 → i
1→ ii, 2→ iii, 3 → i
None of these
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Question 90 MCQ · 2.0 marks
In RANSAC, what is the probability that the algorithm never selects a whole set of inliers from 20
data points (which has 5 inliers in total) for k = 2 time steps? Here, n = 2 is the minimum number of
samples to fit a model.
0.2
0.93
0.87
0
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Question 91 MCQ · 2.0 marks
Suppose we have a codebook of 2048 SIFT visual words. We extract 1000 SIFT descriptors (SIFT is a
128-dimensional feature) from a new image. What is the dimensionality of the BoW (Bag-of-Words)
descriptor?
1000
128
128 × 2048
2048
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Question 92 MCQ · 2.0 marks
1→ iii, 2→ ii, 3 → vi, 4→ i
1→ v, 2→i, 3 → vi, 4→ iii
1→ iii, 2→ i, 3 → ii, 4 → v
1→ ii, 2→ v, 3 → iv, 4→ iii
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Question 93 MCQ · 2.0 marks
1→ vi, 2→ iii, 3→ v, 4 → i
1→ i, 2→ iv, 3 → iii, 4→ ii
1→ i, 2→ v, 3 → iii, 4→ iv
1→ iv, 2→ iii, 3→ ii, 4 → i
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Question 94 MCQ · 2.0 marks
Consider a Convolutional Neural Network which processes an RGB image. It has 128 kernels each
of spatial dimension 5 × 5 in the first layer. They are convolved with a stride 1. This is followed by a
max-pooling layer with stride 2 and kernel size 5 × 5. What would be the receptive field size of a
single neuron in the pooling layer? (Recap: A receptive field is the size of the region in the input
image, which influences the activation of that specific neuron.)
5 × 5
7 × 7
9 × 9
3 × 3
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Question 95 NAT · 2.0 marks
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Question 96 NAT · 2.0 marks
**NOTE:** Enter your answer in two decimal places.
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Question 97 NAT · 2.0 marks
What is the size of the feature map after applying two successive convolution operations with
given parameters? Image size = 64 x 64, Kernel size = 6 x 6, Padding = 1 and Stride = 2. (In
calculation, take floor(x) whenever x is non-integer. If the answer is FxF, write F in the blank).
___________
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Question 98 NAT · 2.0 marks
Let an input to a convolutional layer in a CNN have size Df × Df × M where Df = 64 and M = 16, and
output feature map (after passing input through conv layer) has Df × Df × N size where N = 8. Let
the kernel in the conv layer be k × k where k = 3. Calculate the number of parameters for this
convolution layer. (Assume appropriate padding is applied for all convolutions so the input and
output sizes are equal. Ignore the bias term in the calculation).
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Question 99 NAT · 1.0 marks
Based on the above data, answer the given subquestions.
Fill in the blank for (a)________________
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Question 100 NAT · 1.0 marks
Based on the above data, answer the given subquestions.
Fill in the blank for (b)_____________________
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Question 101 SHORT_TEXT · 2.0 marks
Based on the above data, answer the given subquestions.
Fill in the blank for (a)_____________________
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Question 102 SHORT_TEXT · 1.0 marks
Based on the above data, answer the given subquestions.
Fill in the blank for (b) _____________________
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Question 103 SHORT_TEXT · 1.0 marks
Based on the above data, answer the given subquestions.
Fill in the blank for (c) _____________________
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Question 104 SHORT_TEXT · 2.0 marks
Based on the above data, answer the given subquestions.
Fill in the blank for (a):
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Question 105 SHORT_TEXT · 2.0 marks
Based on the above data, answer the given subquestions.
Fill in the blank for (b):
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Question 106 NAT · 1.0 marks
Based on the above data, answer the given subquestions.
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Question 107 NAT · 1.0 marks
Based on the above data, answer the given subquestions.
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Question 108 NAT · 1.0 marks
Based on the above data, answer the given subquestions.
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Question 109 NAT · 1.0 marks
Based on the above data, answer the given subquestions.
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