MauryaHub PYQ Practice

da5006_2026T1_Q1_NA.pdf

Deep Learning for Computer Vision · Quiz 1 · Jan 2026

← Course papers · Start practice / exam

Questions and published explanations below are available without starting a test. Some questions may not have a published solution yet.

Question 2 MCQ · 1.0 marks

In the Canny edge detector, which is the correct order of major steps?
  1. Non-maximum suppression → Gaussian smoothing → gradient computation → hysteresis thresholding
  2. Gaussian smoothing → gradient computation → non-maximum suppression → hysteresis thresholding
  3. Gradient computation → Gaussian smoothing → hysteresis thresholding → non-maximum suppression
  4. Gaussian smoothing → hysteresis thresholding → non-maximum suppression → gradient computation

A published solution is not available for this question yet.

Question 3 MCQ · 1.0 marks

Which is True?
  1. Nesterov momentum looks ahead and often converges faster than classical momentum.
  2. large fixed learning rate always speeds convergence.
  3. Adam uses only a first-moment estimate of gradients.
  4. None of these.

A published solution is not available for this question yet.

Question 4 MCQ · 1.0 marks

Correct order of SIFT stages is:
  1. Orientation → Descriptor → Localization → Extrema Detection
  2. Extrema Detection → Localization → Orientation → Descriptor
  3. Extrema Detection → Descriptor → Localization → Orientation
  4. None of these.

A published solution is not available for this question yet.

Question 5 MCQ · 1.0 marks

Which of the following 3 × 3 kernels is separable?
  1. [[IMAGE:31839f2e24aa5cc9_4_2]]
    Source diagram or notation
  2. [[IMAGE:31839f2e24aa5cc9_4_3]]
    Source diagram or notation
  3. [[IMAGE:31839f2e24aa5cc9_4_4]]
    Source diagram or notation
  4. [[IMAGE:31839f2e24aa5cc9_4_5]]
    Source diagram or notation

A published solution is not available for this question yet.

Question 6 MCQ · 1.0 marks

Consider the following pyTorch code: [[IMAGE:31839f2e24aa5cc9_5_6]]
Source diagram or notation
  1. tensor([0.09, 0.24, 0.67])
  2. tensor([0.09, 0.24, 0.66])
  3. tensor([0.09, 0.23, 0.68])
  4. tensor([0.10, 0.27, 0.63])

A published solution is not available for this question yet.

Question 7 NAT · 1.0 marks

[[IMAGE:31839f2e24aa5cc9_5_7]]
Source diagram or notation

    A published solution is not available for this question yet.

    Question 8 NAT · 1.0 marks

    [[IMAGE:31839f2e24aa5cc9_6_8]]
    Source diagram or notation

      A published solution is not available for this question yet.

      Question 9 NAT · 1.0 marks

      [[IMAGE:31839f2e24aa5cc9_6_9]]
      Source diagram or notation

        A published solution is not available for this question yet.

        Question 10 NAT · 1.0 marks

        [[IMAGE:31839f2e24aa5cc9_6_10]]
        Source diagram or notation

          A published solution is not available for this question yet.

          Question 11 NAT · 1.0 marks

          [[IMAGE:31839f2e24aa5cc9_7_11]]
          Source diagram or notation

            A published solution is not available for this question yet.

            Question 12 NAT · 1.0 marks

            The sum of the degrees of freedom for translation and affine transformations in 3D is __

              A published solution is not available for this question yet.

              Question 13 NAT · 1.0 marks

              [[IMAGE:31839f2e24aa5cc9_8_12]]
              Source diagram or notation

                A published solution is not available for this question yet.

                Question 14 NAT · 1.0 marks

                [[IMAGE:31839f2e24aa5cc9_8_13]]
                Source diagram or notation

                  A published solution is not available for this question yet.

                  Question 15 NAT · 1.0 marks

                  [[IMAGE:31839f2e24aa5cc9_9_14]]
                  Source diagram or notation

                    A published solution is not available for this question yet.