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Question 249 MCQ · 2.0 marks
What is the role of the Gaussian blur in the Canny edge detection algorithm, and how does the
choice of the standard deviation parameter affect the performance of edge detection? Which
option do you think best describes the role of the Gaussian blur and its relationship with the
standard deviation parameter in the Canny edge detection algorithm?
6406533045014.
The Gaussian blur helps to reduce noise in the image and smooth out pixel
intensity variations.The standard deviation parameter controls the amount of blurring applied,
with higher values resulting in more aggressive smoothing, which may lead to loss of edge detail.
6406533045015.
The Gaussian blur enhances edge contrast in the image and amplifies pixel
intensity gradients. The standard deviation parameter determines the width of the blur kernel,
with larger values resulting in sharper edges and finer details preserved.
6406533045016.
The Gaussian blur serves to highlight high-frequency components in the
image and accentuate edge boundaries. The standard deviation parameter dictates the level of
detail preserved, with smaller values preserving finer details but potentially amplifying noise.
6406533045017.
The Gaussian blur functions to dilate edge regions in the image and expand
the edge boundaries.The standard deviation parameter controls the extent of dilation, with higher
values resulting in broader edge regions but also increasing the risk of false edge detections.
A published solution is not available for this question yet.
Consider the following statements and choose the correct option:
(i) Grad-CAM overcame the problems of previous works to work better in images with occlusion
and multiple instances of objects.
(ii) Grad-CAM ++ uses a weighted average of pixels to solve the problem of activation maps with
lesser spatial footprint getting suppressed.
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Only statement (i) is True
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Only statement (ii) is True
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Both the statements are True
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None of the statements is True
A published solution is not available for this question yet.
Which of the following is the correct sequence of steps of the SIFT algorithm?
1. Using the Taylor series expansion of the scale-space function, localize the key points.
2. Estimate the orientation of keypoints using the local image gradients.
3. Find the edge points using the eigenvalues of the Hessian and reject these points.
4. Find a raw SIFT descriptor vector using the gradient orientations of the neighborhood around
the key points.
5. Detect extreme points in scale space using an approximation of the Laplacian of Gaussian.
6406533045035.
5, 2, 1, 3, 4
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5, 1, 3, 2, 4
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1, 3, 2, 4, 5
6406533045038.
1, 5, 3, 2, 4
A published solution is not available for this question yet.
Consider a vanilla transformer network at time step ’t’ shown below:
For each of the entities above, enter the appropriate Model component from the figure that
corresponds to it:
Based on the above data, answer the given subquestions.
Enter the answer for Model component 1
**NOTE:** Enter the exact answer without any space in the beginning or at the end.
A published solution is not available for this question yet.
Consider a vanilla transformer network at time step ’t’ shown below:
For each of the entities above, enter the appropriate Model component from the figure that
corresponds to it:
Based on the above data, answer the given subquestions.
Enter the answer for Model component 2
**NOTE:** Enter the exact answer without any space in the beginning or at the end.
A published solution is not available for this question yet.
Consider a vanilla transformer network at time step ’t’ shown below:
For each of the entities above, enter the appropriate Model component from the figure that
corresponds to it:
Based on the above data, answer the given subquestions.
Enter the answer for Model component 3
**NOTE:** Enter the exact answer without any space in the beginning or at the end.
A published solution is not available for this question yet.
Consider a vanilla transformer network at time step ’t’ shown below:
For each of the entities above, enter the appropriate Model component from the figure that
corresponds to it:
Based on the above data, answer the given subquestions.
Enter the answer for Model component 4
**NOTE:** Enter the exact answer without any space in the beginning or at the end.
A published solution is not available for this question yet.
Consider a vanilla transformer network at time step ’t’ shown below:
For each of the entities above, enter the appropriate Model component from the figure that
corresponds to it:
Based on the above data, answer the given subquestions.
Enter the answer for Model component 5
**NOTE:** Enter the exact answer without any space in the beginning or at the end.
A published solution is not available for this question yet.
Consider a vanilla transformer network at time step ’t’ shown below:
For each of the entities above, enter the appropriate Model component from the figure that
corresponds to it:
Based on the above data, answer the given subquestions.
Enter the answer for Model component 6
**NOTE:** Enter the exact answer without any space in the beginning or at the end.
A published solution is not available for this question yet.
Consider a vanilla transformer network at time step ’t’ shown below:
For each of the entities above, enter the appropriate Model component from the figure that
corresponds to it:
Based on the above data, answer the given subquestions.
Enter the answer for Model component 7
**NOTE:** Enter the exact answer without any space in the beginning or at the end.
A published solution is not available for this question yet.
Consider a vanilla transformer network at time step ’t’ shown below:
For each of the entities above, enter the appropriate Model component from the figure that
corresponds to it:
Based on the above data, answer the given subquestions.
Enter the answer for Model component 8
**NOTE:** Enter the exact answer without any space in the beginning or at the end.
A published solution is not available for this question yet.
Consider a vanilla transformer network at time step ’t’ shown below:
For each of the entities above, enter the appropriate Model component from the figure that
corresponds to it:
Based on the above data, answer the given subquestions.
Enter the answer for Model component 9
**NOTE:** Enter the exact answer without any space in the beginning or at the end.
A published solution is not available for this question yet.
Consider a Faster R-CNN object detection framework, which is being trained with a minibatch size
of 2. The RPN produces 512 proposals, out of which 5 and 8 of them are foreground proposals,
corresponding to each image in the mini-batch. For a single mini-batch update, the bounding box
regression loss will be computed ______________________ times.
A published solution is not available for this question yet.
Generative Adversarial Networks (GANs) are widely used to generate new images. Many GAN-
based architectures have been developed over the years to improve the quality of
generation.Progressive GAN, a popular architecture among many others, uses a technique called
progressive growing. Which one of the options below is the purpose for progressive growing?
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To grow the range of loss function
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To gradually train the generator by increasing the resolution of images being
generated in iterations
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To iteratively train the generator to create 3D images from 2D ones
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To shrink the size of the generator model so generator become faster
A published solution is not available for this question yet.
**SPG**
**Section Id :** 64065364139
**Section Number :** 11
**Section type :** Online
**Mandatory or Optional :** Mandatory
**Number of Questions :** 24
**Number of Questions to be attempted :** 24
**Section Marks :** 35
**Display Number Panel :** Yes
**Section Negative Marks :** 0
**Group All Questions :** No
**Enable Mark as Answered Mark for Review and**
No
**Clear Response :**
**Maximum Instruction Time :** 0
A published solution is not available for this question yet.
