Figure from the original question paper

(C) 0.18,−1.5, 0, 0.20, 0,−0.015

(A) 0.18, 1.5, 1.5, 0.20,−1.5,−0.015

Match the derivative of activation functions f(x) with their counterparts on the right column accordingly. | 1) Leaky ReLU | i) f(x)(1 - f(x)) | |---|---| | 2) Tanh | ii) 0 if x 0 | | 3) ReLU | iii) 1 - f(x)^2 | | 4) Sigmoid | iv) 0.01 if x 0 | | | v) 0 if x \geqslant 0 and 1 if x < 0 | | | vi) 0.01 if x \geqslant 0 and 1 if x < 0 |

(D) 1→ iv, 2→ iii, 3→ ii, 4 → i

(A) 1→ vi, 2→ iii, 3→ v, 4 → i

(B) 1→ i, 2 →iv, 3 → iii, 4→ ii

(C) 1→ i, 2 →v, 3 → iii, 4→ iv

Figure from the original question paper

(A) Option figure from the original question paper

(C) Option figure from the original question paper

(B) Option figure from the original question paper

(D) Option figure from the original question paper

Figure from the original question paper

(A) Option figure from the original question paper

(D) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

Deep Learning for Computer Vision Quiz 1 PYQ Sep 2024: 27 Oct 2024

Question 1

+2 marksOne correct option

A
54
B
10
C
0
D
18

Show answer

Correct answer

C
0

Question 1 on its own page

Question 2

+2 marksOne correct option

Which of the following statements is false?

A
Linear contrast stretching is a point operation.
B
Moving average is an example of local operation.
C
Convolution in the spatial domain can be obtained through addition in the frequency domain.
D
All of these.

Show answer

Correct answer

C
Convolution in the spatial domain can be obtained through addition in the frequency domain.

Question 2 on its own page

Question 3

+2 marksOne correct option

Which of the following statements is True

A
Gaussian filter is Separable filter because it is non linear.
B
Median filter is a non separable filter
C
Gaussian filter is a High Pass filter.
D
Mean filter is a non separable filter because it is non linear

Show answer

Correct answer

B
Median filter is a non separable filter

Question 3 on its own pageAlso asked in Quiz 1 25 Feb 2024, Quiz 1 7 Jul 2024, Quiz 1 23 Feb 2025

Question 4

+2 marksOne correct option

A
0.18, 1.5, 1.5, 0.20,−1.5,−0.015
B
0.92, 2.5, 1, 2.5, 2.5, 1
C
0.18,−1.5, 0, 0.20, 0,−0.015
D
0.91, 1, 0, 0.2, 1, 1.7

Show answer

Correct answer

C
0.18,−1.5, 0, 0.20, 0,−0.015

Question 4 on its own page

Question 5

+2 marksOne correct option

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

A
6→ 1→ 4→ 5 → 2
B
3→ 1→ 6→ 4 → 2
C
3→ 5→ 1→ 4 → 2
D
6→ 8→ 5→ 7 → 2

Show answer

Correct answer

B
3→ 1→ 6→ 4 → 2

Question 5 on its own pageAlso asked in End Term 22 Dec 2024

Question 6

+2 marksOne correct option

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

A
4→ 2→ 1→ 3
B
3→ 2→ 4→ 1
C
3→ 1→ 2→ 4
D
None of these.

Show answer

Correct answer

B
3→ 2→ 4→ 1

Question 6 on its own pageAlso asked in Quiz 1 7 Jul 2024, Quiz 1 23 Feb 2025

Question 7

+2 marksOne correct option

A
1→ iii, 2→ i, 3 → ii
B
1→ iii, 2→ ii, 3 → i
C
1→ ii, 2→ iii, 3 → i
D
None of these.

Show answer

Correct answer

A
1→ iii, 2→ i, 3 → ii

Question 7 on its own page

Question 8

+2 marksOne correct option

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?

A
1000
B
128
C
128 × 2048
D
2048

Show answer

Correct answer

D
2048

Question 8 on its own pageAlso asked in Quiz 1 25 Feb 2024, Quiz 1 7 Jul 2024, Quiz 1 23 Feb 2025

Question 9

+2 marksOne correct option

Match the derivative of activation functions $f(x)$ with their counterparts on the right column accordingly.

1) Leaky ReLU	i) $f(x)(1 - f(x))$
2) Tanh	ii) 0 if $x < 0$ and 1 if $x > 0$
3) ReLU	iii) $1 - f(x)^2$
4) Sigmoid	iv) 0.01 if $x < 0$ and 1 if $x > 0$
	v) 0 if $x \geqslant 0$ and 1 if $x < 0$
	vi) 0.01 if $x \geqslant 0$ and 1 if $x < 0$

A
1→ vi, 2→ iii, 3→ v, 4 → i
B
1→ i, 2 →iv, 3 → iii, 4→ ii
C
1→ i, 2 →v, 3 → iii, 4→ iv
D
1→ iv, 2→ iii, 3→ ii, 4 → i

Show answer

Correct answer

D
1→ iv, 2→ iii, 3→ ii, 4 → i

Question 9 on its own pageAlso asked in End Term 22 Dec 2024, Quiz 1 23 Feb 2025, End Term 31 Aug 2025

Question 10

+2 marksOne correct option

A
5 × 5
B
7 × 7
C
9 × 9
D
3 × 3

Show answer

Correct answer

C
9 × 9

Question 10 on its own page

Question 11

+2 marksOne or more correct options

Which of the following statements are false?

Select all that apply.

A
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.
B
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.
C
Adagrad is an optimization algorithm introduced to overcome the diminishing learning rate problem in techniques like RMSProp.
D
ADAM is introduced to solve problems in RMSProp by combining RMSProp and Adagrad techniques.

Show answer

Correct answers

C
Adagrad is an optimization algorithm introduced to overcome the diminishing learning rate problem in techniques like RMSProp.
D
ADAM is introduced to solve problems in RMSProp by combining RMSProp and Adagrad techniques.

Question 11 on its own pageAlso asked in Quiz 1 23 Feb 2025, Quiz 1 13 Jul 2025

Question 12

+2 marksOne or more correct options

Select all that apply.

A
B
C
D

Show answer

Correct answers

A
C

Question 12 on its own page

Question 13

+2 marksOne or more correct options

Select all that apply.

A
B
C
D

Show answer

Correct answers

A
D

Question 13 on its own page

Question 14

+2 marksOne or more correct options

Which of the following statements are true? (Select all that apply)

Select all that apply.

A
B
C
D

Show answer

Correct answers

A
D

Question 14 on its own page

Question 15

+2 marksOne or more correct options

Which of the following are true? (Select all that apply)

Select all that apply.

A
B
C
D

Show answer

Correct answers

A
B

Question 15 on its own page

Question 16

+0.5 marksWritten answer

The gradient of an image I, points in the direction of 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 solution to this is (c) _________ the image prior to computing gradients.
Based on the above data, answer the given subquestions.

Enter the correct answer for (a).
NOTE: Enter the exact answer without any space in the beginning or at the end.

Show answer

Correct answer: high

Question 16 on its own page

Question 17

+0.5 marksWritten answer

The gradient of an image I, points in the direction of 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 solution to this is (c) _________ the image prior to computing gradients.
Based on the above data, answer the given subquestions.

Enter the correct answer for (b).
NOTE: Enter the exact answer without any space in the beginning or at the end.

Show answer

Correct answer: high

Question 17 on its own page

Question 18

+1 markWritten answer

The gradient of an image I, points in the direction of 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 solution to this is (c) _________ the image prior to computing gradients.
Based on the above data, answer the given subquestions.

Enter the correct answer for (c).
NOTE: Enter the exact answer without any space in the beginning or at the end.

Show answer

Correct answer: smooth

Question 18 on its own page

Question 19

+1 markWritten answer

In Canny edge detection, large value of the Gaussian kernel spread, σ leads to (a) __________ edges, and small value of σ leads to (b) __________ edges.
Based on the above data, answer the given subquestions.

Enter the correct answer for (a).
NOTE: Enter the exact answer without any space in the beginning or at the end.

Show answer

Correct answer: thick or coarse or large scale

Question 19 on its own page

Question 20

+1 markWritten answer

In Canny edge detection, large value of the Gaussian kernel spread, σ leads to (a) __________ edges, and small value of σ leads to (b) __________ edges.
Based on the above data, answer the given subquestions.

Enter the correct answer for (b) .
NOTE: Enter the exact answer without any space in the beginning or at the end.

Show answer

Correct answer: fine

Question 20 on its own page

Question 21

+0.5 marksNumerical answer

Based on the above data, answer the given subquestions.

Enter the correct answer for a11

Show answer

Correct answer: 1

Question 21 on its own page

Question 22

+0.5 marksNumerical answer

Based on the above data, answer the given subquestions.

Enter the correct answer for a12

Show answer

Correct answer: 0.5

Question 22 on its own page

Question 23

+0.5 marksNumerical answer

Based on the above data, answer the given subquestions.

Enter the correct answer for a21

Show answer

Correct answer: 0

Question 23 on its own page

Question 24

+0.5 marksNumerical answer

Based on the above data, answer the given subquestions.

Enter the correct answer for a22

Show answer

Correct answer: 1

Question 24 on its own page

Question 25

+2 marksNumerical answer

Consider the following numpy array

$\text{arr} = \begin{bmatrix} 0 & 1 \\ 2 & 3 \\ 4 & 5 \end{bmatrix}$

Now consider the following operations

$\text{flattened\_arr} = \text{arr.flatten()}$

$\text{flattened\_arr[0]} = 10$

Write the value of arr[0]

Show answer

Correct answer: 0

Question 25 on its own page

Question 26

+2 marksNumerical answer

Consider the following numpy array

$\text{arr} = \begin{bmatrix} 0 & 1 \\ 2 & 3 \\ 4 & 5 \end{bmatrix}$

Now consider the following operations

$\text{raveled\_arr} = \text{arr.ravel()}$

$\text{raveled\_arr[0]} = 10$

Write the value of arr[0]

Show answer

Correct answer: 10

Question 26 on its own page

Question 27

+2 marksNumerical answer

Consider the grayscale image shown below:

$\begin{bmatrix} 20 & 30 & 25 & 30 & 40 \\ 45 & 10 & 40 & 35 & 20 \\ 15 & 20 & 30 & 25 & 50 \\ 50 & 40 & 35 & 15 & 30 \\ 10 & 35 & 25 & 20 & 45 \end{bmatrix}$

If a median filter with a $3 \times 3$ neighborhood is applied to this image with a stride of 1 and no padding, __________ will be the value at position $(2, 2)$ in the filtered image? (Assume that the top-left pixel coordinate of output image is addressed as $(1, 1)$ )?