In the Canny edge detector, which is the correct order of major steps?
Non-maximum suppression → Gaussian smoothing → gradient computation → hysteresis thresholding
Gaussian smoothing → gradient computation → non-maximum suppression → hysteresis thresholding
Gradient computation → Gaussian smoothing → hysteresis thresholding → non-maximum suppression
Gaussian smoothing → hysteresis thresholding → non-maximum suppression → gradient computation
Which is True?
Nesterov momentum looks ahead and often converges faster than classical momentum.
large fixed learning rate always speeds convergence.
Adam uses only a first-moment estimate of gradients.
None of these.
Correct order of SIFT stages is:
Orientation → Descriptor → Localization → Extrema Detection
Extrema Detection → Localization → Orientation → Descriptor
Extrema Detection → Descriptor → Localization → Orientation
None of these
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The IIT Madras BS Deep Learning for Computer Vision (Deep Learning for Computer Vision) Quiz 1 paper sat on 19 Jul 2026, in the May 2026 term: 13 questions for 13 marks in 120 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.
| Feature | Deep Learning for Computer Vision Quiz 1 19 Jul 2026 at a glance |
|---|---|
| Term | May 2026 term |
| Subject | Deep Learning for Computer Vision |
| Course code | BSDA5006 |
| Questions | 13 |
| Marks | 13 |
| Duration | 120 min |
| MCQ | 5 |
| Numerical | 8 |
| Official paper | Deep Learning For Computer Vision 16 Jul 26 |
| Negative marking | No negative marking. |
| Updated |
