Deep Learning for Computer Vision · Quiz 2 · Sep 2025

Which of the following architectures introduced the ROI pooling layer for extracting fixed-size feature maps from variable-size region proposals?

Which object detection method first integrated a Region Proposal Network (RPN) to generate region proposals within the detection model?

Which architecture performs object detection as a single regression problem from image pixels to bounding boxes and class probabilities?

Consider the following statements P and Q regarding AlexNet and choose the correct option: **(P)** In AlexNet, a trainable Local Response Normalization Layers were introduced to emulate the competitive nature of real neurons, where highly active neurons suppress the activity of neighboring neurons, creating competition among different kernel outputs. **(Q)** In AlexNet, a total of 8 Convolutional layers contain only about 6-7% of the total parameters hence account for the least computation.

Consider two bounding boxes in an image: - Box A: top-left at (0, 0), bottom-right at (12, 12) - Box B: top-left at (4, 4), bottom-right at (16, 16) What is the Intersection over Union (IoU) between these two boxes ______________

Which one of the following statements is **True**?

Which of the following techniques help mitigate the **vanishing gradient problem** in recurrent neural networks? 1. Gradient clipping 2. Use of ReLU activation to preserve gradient flow 3. Use of LSTM units 4. Data augmentation (e.g., reversing input sequence)

Which of the following techniques help mitigate the **exploding gradient problem** in recurrent neural networks? 1. Gradient clipping 2. Use of Sigmoid activation to prevent gradient growth 3. Use of LSTM units 4. Data augmentation (e.g., reversing input sequence)

Which one of the following statements is false? (Pick the most appropriate one.)

Which of the following is true regarding Hard Attention and Soft Attention?

Which of the following are **False:**

Which techniques help mitigate exploding/vanishing gradients in RNNs? (Select all that apply.)

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Number of Parameters:_____________

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Computational Cost:________________

Using the same dimensions specified in the previous question main data, 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 seperable convolution: __________

Using the same dimensions specified in the previous question main data, 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 seperable convolution:____________

Using the same dimensions specified in the previous question main data, 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:_____________

Using the same dimensions specified in the previous question main data, 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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Number of weights in Weight Matrix U1 is:_____________

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Number of weights in Weight Matrix V1 is:_____________

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Number of weights in Weight Matrix U2 is:_____________

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Number of weights in Weight Matrix V2 is:_____________

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Number of weights in Weight Matrix W is:_____________

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