da5006_2025T1_Q1_NA.pdf
Deep Learning for Computer Vision · Quiz 1 · Jan 2025
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Questions and published explanations below are available without starting a test. Some questions may not have a published solution yet.
Question 152 NAT · 2.0 marks
In a semantic role labeling evaluation, a model has identified 100 correct semantic roles out of 120
total semantic roles it predicted. What is the precision of the model as a decimal (rounded to two
decimal places)?
A published solution is not available for this question yet.
Question 154 NAT · 2.0 marks
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A published solution is not available for this question yet.
Question 155 NAT · 2.0 marks
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A published solution is not available for this question yet.
Question 156 NAT · 2.0 marks
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A published solution is not available for this question yet.
Question 157 NAT · 2.0 marks
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A published solution is not available for this question yet.
Question 158 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 = 3 x 3, Padding = 2 and Stride = 3. (In
calculation, take floor(x) whenever x is non-integer. If the answer is FxF, write F in the blank).
A published solution is not available for this question yet.
Question 159 NAT · 2.0 marks
Let an input to a convolutional layer in a CNN have size Df × Df × M where Df = 32 and M = 100, and
output feature map (after passing input through conv layer) has Df × Df × N size where N = 64. Let
the kernel in the conv layer be k × k where k = 5. 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)._________________
A published solution is not available for this question yet.
Question 160 NAT · 2.0 marks
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A published solution is not available for this question yet.
Question 161 MCQ · 2.0 marks
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119.33
110.5
134.25
0
A published solution is not available for this question yet.
Question 162 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
A published solution is not available for this question yet.
Question 163 MCQ · 2.0 marks
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0.18, 1.5, 1.5, 0.20,−1.5,−0.015
0.92, 2.5, 1, 2.5, 2.5, 1
0.18,−1.5, 0, 0.20, 0,−0.015
0.91, 1, 0, 0.2, 1, 1.7
A published solution is not available for this question yet.
Question 164 MCQ · 2.0 marks
Identify the correct sequence of steps in a Canny edge detection pipeline. Steps are listed below:
1. Compute gradient magnitude and direction
2. Connect individual components
3. Smoothen the image
4. Threshold into strong, weak, or no edge
5. Gaussian Filter and Hysteresis
6. Non-maximum suppression
7. Apply derivative to get edges
6→ 1→ 4→ 5 → 2
3→ 1→ 6→ 4 → 2
3→ 5→ 1→ 4 → 2
6→ 1→ 5→ 7 → 2
A published solution is not available for this question yet.
Question 165 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.
A published solution is not available for this question yet.
Question 166 MCQ · 2.0 marks
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1→ iii, 2→ i, 3 → ii
1→ iii, 2→ ii, 3 → i
1→ ii, 2→ iii, 3 → ii
None of these
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Question 167 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
A published solution is not available for this question yet.
Question 168 MCQ · 2.0 marks
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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
A published solution is not available for this question yet.
Question 169 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
A published solution is not available for this question yet.
Question 170 MCQ · 2.0 marks
Consider the following two statements. Which of the following statements are true?
(a) Convolution operator is both commutative and associative.
(b) Fourier transform of a convolved image FT (a * b), is not the product of the Fourier transform of
the constituent images FT(a) x FT(b)
a and b
not a but b
a but not b
Neither a nor b
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Question 171 MCQ · 2.0 marks
A 3x3 kernel generates an output image of dimension 20x20 after convolution. The (approximate)
number of computations performed to obtain this output image is:
3600
180
400
9
A published solution is not available for this question yet.
Question 172 MCQ · 2.0 marks
Which of the following statements are True?
Vanilla Gradient Descent converges faster than Momentum-based GD.
Momentum based GD oscillates around minima before convergence.
Noise in Stochastic Gradient descent weight updates – can lead to faster
convergence.
None of these
A published solution is not available for this question yet.
Question 173 MCQ · 2.0 marks
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1→ iii, 2→ iv, 3→ i, 4 → ii
1→ iii, 2→ i, 3 → ii, 4 →v
1→ iii, 2→ iv, 3→ v, 4 → ii
1→ iv, 2→ iii, 3→ i, 4 → ii
A published solution is not available for this question yet.
Question 174 MSQ · 2.0 marks
Which of the following statements is **false**?
Linear contrast stretching is a local operation.
Moving average is an example of local operation.
Convolution in the spatial domain can be obtained through addition in the
frequency domain.
All of these.
A published solution is not available for this question yet.
Question 175 MSQ · 2.0 marks
Which of the following statements are **false**?
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.
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.
Adagrad is an optimization algorithm introduced to overcome the diminishing
learning rate problem in techniques like RMSProp.
ADAM is introduced to solve problems in RMSProp by combining RMSProp and
Adagrad techniques.
A published solution is not available for this question yet.
Question 176 MSQ · 2.0 marks
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A published solution is not available for this question yet.
Question 177 MSQ · 2.0 marks
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(a)
(b)
(c)
(d)
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Question 178 MSQ · 2.0 marks
Which of the following statements are **true**? (Select all that apply)
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[[IMAGE:22769d58a07a5853_12_17]]
[[IMAGE:22769d58a07a5853_12_18]]
[[IMAGE:22769d58a07a5853_12_19]]
A published solution is not available for this question yet.
Question 179 MSQ · 2.0 marks
Which of the following are **true**? (Select all that apply)
[[IMAGE:22769d58a07a5853_12_20]]
[[IMAGE:22769d58a07a5853_12_21]]
[[IMAGE:22769d58a07a5853_12_22]]
[[IMAGE:22769d58a07a5853_12_23]]
A published solution is not available for this question yet.
Question 180 SHORT_TEXT · 1.0 marks
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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.
A published solution is not available for this question yet.
Question 181 SHORT_TEXT · 1.0 marks
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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.
A published solution is not available for this question yet.
Question 182 SHORT_TEXT · 1.0 marks
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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.
A published solution is not available for this question yet.
Question 183 SHORT_TEXT · 1.0 marks
[[IMAGE:22769d58a07a5853_14_25]]
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.
A published solution is not available for this question yet.
Question 184 NAT · 0.5 marks
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Based on the above data answer the given subquestions.
a11= ____________
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Question 185 NAT · 0.5 marks
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Based on the above data answer the given subquestions.
a12= ____________
A published solution is not available for this question yet.
Question 186 NAT · 0.5 marks
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Based on the above data answer the given subquestions.
a21= ____________
A published solution is not available for this question yet.
Question 187 NAT · 0.5 marks
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Based on the above data answer the given subquestions.
a22= ____________
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Question 188 NAT · 1.0 marks
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A published solution is not available for this question yet.
Question 189 NAT · 1.0 marks
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A published solution is not available for this question yet.