Deep Learning for Computer Vision · Quiz 2 · Jan 2025

Which one of the following object detection networks uses an ROI pooling layer?

Which one of the following statements is False?

In object detection using CNNs, which loss function is typically used to optimize the confidence scores assigned to predicted bounding boxes, aiming to ensure high confidence for correct detec- tions and penalize false positives?

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):

In object detection using CNNs, what is the challenge addressed by the non-maximum suppression (NMS) technique?

Which of the following techniques help control the exploding or vanishing gradient problem in recurrent neural networks? 1. Gradient clipping 2. Use ReLU activation funtion to control exploding gradient and sigmoid activation to control vanishing gradients 3. Use Long Short-Term Memory (LSTM) 4. Use data augmentation methods (for example, reversing the input sequence)

State whether the given statement is True or False: While training an object detection algorithm, you need a training set that contains many pictures of the objects you want to detect. However, bounding boxes are not typically provided in the training set, since architectures like Faster-RCNN and YOLO can learn to detect the object by themselves.

How are saliency maps generated in the context of Convolutional Neural Networks (CNNs)?

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:

Calculate the mean Average Precision (mAP) 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?

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

Which of the following statements is **True** (select all that apply):

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

Which of the following are **true?**

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 256 and 128 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.

Based on the above data, answer the given subquestions.

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Number of Parameters: __________________

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Computational Cost: __________________

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Number of parameters for depthwise convolution: __________________

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Computational Cost for depthwise convolution: ________________

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Number of parameters for pointwise convolution: _________________

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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: ________________