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Deep Learning for Computer Vision · Quiz 2 · Sep 2024

Deep Learning for Computer Vision · Quiz 2 · Sep 2024

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

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Question 132 NAT · 2.0 marks

Source diagram or notation
Based on the above data, answer the given subquestions.
Number of Parameters: ______________

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Question 133 NAT · 2.0 marks

Source diagram or notation
Based on the above data, answer the given subquestions.
Computational Cost: ______________

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Question 134 NAT · 2.0 marks

Source diagram or notation
Calculate the number of parameters and computational cost, but make use of **Depthwise** **Seperable convolution** and **pointwise convolution** instead of standard convolution. Based on the above data, answer the given subquestions.
Number of parameters for depthwise convolution: ______________

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Question 135 NAT · 2.0 marks

Source diagram or notation
Calculate the number of parameters and computational cost, but make use of **Depthwise** **Seperable convolution** and **pointwise convolution** instead of standard convolution. Based on the above data, answer the given subquestions.
Computational Cost for depthwise convolution: ______________

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Question 136 NAT · 2.0 marks

Source diagram or notation
Calculate the number of parameters and computational cost, but make use of **Depthwise** **Seperable convolution** and **pointwise convolution** instead of standard convolution. Based on the above data, answer the given subquestions.
Number of parameters for pointwise convolution: ______________

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Report a correction for Q136

Question 137 NAT · 2.0 marks

Source diagram or notation
Calculate the number of parameters and computational cost, but make use of **Depthwise** **Seperable convolution** and **pointwise convolution** instead of standard convolution. Based on the above data, answer the given subquestions.
Computational cost for pointwise convolution: ______________

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Question 138 MCQ · 2.0 marks

Source diagram or notation
6406533487212.Source diagram or notation
6406533487213.Source diagram or notation
6406533487214.Source diagram or notation
6406533487215.Source diagram or notation

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Question 139 MCQ · 2.0 marks

Which one of the following object detection networks uses an ROI pooling layer?
6406533487216.
Fast R-CNN
6406533487217.
R-CNN
6406533487218.
YOLO
6406533487219.
All of these

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Question 140 MCQ · 2.0 marks

Consider an object detection system evaluated on a dataset consisting of 1000 images. The system makes 1500 predictions across these images, and for each image, there are annotated ground truth bounding boxes. The system’s precision-recall curve is calculated, and the precision at different recall levels for one of the classes is as follows:
Source diagram or notation
Calculate the Average Precision (AP) for this class using the 11-point interpolation method, which averages the precision values at recall levels {0.0, 0.1, 0.2, ..., 1.0}. The precision at recall 0.0 can be assumed to be 1.0. Additionally, the system’s AP values for the other two classes are as follows: - AP for class 2: 0.78 - AP for class 3: 0.72 Based on these AP values, what is the mean Average Precision (mAP) across all three classes?
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0.70
6406533487221.
0.69
6406533487222.
0.73
6406533487223.
0.76

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Question 141 MCQ · 2.0 marks

Source diagram or notation
6406533487224.
1→iv, 2→ii, 3→v, 4→i
6406533487225.
1→iv, 2→iii, 3→v, 4→ii
6406533487226.
1→iv, 2→v, 3→ii, 4→iii
6406533487227.
1→iii, 2→v, 3→i, 4→ii

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Question 142 MCQ · 2.0 marks

Which one of the following statements is False?
6406533487228.
In a max pooling layer, the unit that gets selected (maximum entry) in forward propagation gets all the gradient during backpropagation.
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Residual blocks are used in ResNets to avoid the vanishing gradient problem.
6406533487230.
Feature maps of initial layers of a CNN capture more abstract concepts than the feature maps of final layers.
6406533487231.
Dilated convolution increases the receptive field size when compared to the standard convolution operator.

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Question 143 MCQ · 2.0 marks

Source diagram or notation
6406533487233.
Sigmoid of alignment scores
6406533487234.
Softmax of alignment scores
6406533487235.
ReLU of alignment scores
6406533487236.
None of these

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Question 144 MCQ · 2.0 marks

Which one of the following statements is **false?**
6406533487237.
Attention mechanisms cannot be applied to the bidirectional RNN model
6406533487238.
An image captioning network cannot be trained end-to-end even though we are using 2 different modalities to train the network
6406533487239.
One of the key components in the vanilla transformer are the recurrent connections that help them to deal with variable input length.
6406533487240.
None of these

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Question 145 MCQ · 2.0 marks

Consider two 12×12 bounding boxes(one on the upper left and one of the lower right) in an images with an overlapping region of 8 × 8. The Intersection over Union (IoU) between the two boxes is (choose the closest value):
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21%
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25%
6406533487247.
28%
6406533487248.
37%

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Question 146 MCQ · 2.0 marks

Which one of the following statements is **true?**
6406533487249.
Attention mechanisms cannot be applied to the bidirectional RNN model
6406533487250.
An image captioning network cannot be trained end-to-end even though we are using 2 different modalities to train the network
6406533487251.
One of the key components in the vanilla transformer are the recurrent connections that help them to deal with variable input length.
6406533487252.
None of these

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Question 147 MCQ · 2.0 marks

Source diagram or notation
6406533487253.
1→ iv, 2→ iii, 3→ ii, 4 → i
6406533487254.
1→ ii, 2→ iii, 3 → i, 4 →iv
6406533487255.
1→ iv, 2→ iii, 3→ i, 4 → ii
6406533487256.
1→ ii, 2→ iv, 3 → iii, 4→ i

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Question 148 MCQ · 2.0 marks

Source diagram or notation
6406533487261.
1→ iii, 2→ iv, 3→ ii, 4 → i
6406533487262.
1→ iv, 2→ i, 3 → iii, 4→ ii
6406533487263.
1→ i, 2→ ii, 3 → iii, 4→ iv
6406533487264.
1→ i, 2→ iii, 3 → ii, 4→ iv

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Question 149 MCQ · 2.0 marks

Source diagram or notation
6406533487265.
1→iii, 2→ii, 3→v, 4→iv,5→i
6406533487266.
1→iii, 2→iv, 3→i, 4→ii,5→v
6406533487267.
1→i, 2→iii, 3→iv, 4→ii,5→v
6406533487268.
1→v, 2→iv, 3→iii, 4→i,5→ii

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Question 150 MSQ · 2.0 marks

Which of the following are **true:**
6406533487241.Source diagram or notation
6406533487242.Source diagram or notation
6406533487243.Source diagram or notation
6406533487244.Source diagram or notation

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Question 151 MSQ · 2.0 marks

Which of the following is true regarding Hard Attention and Soft Attention?
6406533487257.
Soft Attention is smooth and differentiable
6406533487258.
Variance reduction techniques are used to train Soft Attention models
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Soft Attention is computationally cheaper than Hard Attention when the source input is large
6406533487260.
The inference (test time) overhead is low in Hard Attention when compared to Soft Attention models

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Question 152 NAT · 2.0 marks

Consider a Faster R-CNN object detection framework, which is being trained with a mini-batch size of 2. The RPN produces 512 proposals, out of which 5 and 95 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.

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Question 153 NAT · 2.0 marks

The RNN given below is used for classification:
Source diagram or notation
Based on the above data, answer the given subquestions.
Number of weights in Weight Matrix U1 is: ______________

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Question 154 NAT · 2.0 marks

The RNN given below is used for classification:
Source diagram or notation
Based on the above data, answer the given subquestions.
Number of weights in Weight Matrix V1 is: ______________

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Question 155 NAT · 2.0 marks

The RNN given below is used for classification:
Source diagram or notation
Based on the above data, answer the given subquestions.
Number of weights in Weight Matrix U2 is: ______________

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Report a correction for Q155

Question 156 NAT · 2.0 marks

The RNN given below is used for classification:
Source diagram or notation
Based on the above data, answer the given subquestions.
Number of weights in Weight Matrix V2 is: ______________

A published solution is not available for this question yet.

Report a correction for Q156