Deep Learning for Computer Vision Quiz 2: 23 November 2025 (September 2025 term)

48 more questions in this paper

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1. Q4Which architecture performs object detection as a single regression problem from image pi…2 marks
2. Q5Question 52 marks
3. Q6Consider the following statements P and Q regarding AlexNet and choose the correct option…2 marks
4. Q7Consider two bounding boxes in an image:\ Box A: top-left at (0, 0), bottom-right at (12,…2 marks
5. Q8Which one of the following statements is True?2 marks
6. Q9Match the following computer vision tasks to situations: | 1) Instance Segmentation | i) …2 marks
7. Q10Match the following: | 1) One-to-many RNN architecture | i) Sentiment analysis of Netflix…2 marks
8. Q11Question 112 marks
9. Q12Which of the following techniques help mitigate the vanishing gradient problem in recurre…2 marks
10. Q13Which of the following techniques help mitigate the exploding gradient problem in recurre…2 marks
11. Q14Which one of the following statements is false? (Pick the most appropriate one.)2 marks
12. Q15Question 152 marks
13. Q16Which of the following is true regarding Hard Attention and Soft Attention?3 marks
14. Q17Which of the following are False:3 marks
15. Q18Which techniques help mitigate exploding/vanishing gradients in RNNs? (Select all that ap…3 marks
16. Q19Based on the above data, answer the given subquestions.0.5 marks
17. Q20Based on the above data, answer the given subquestions.0.5 marks
18. Q21Based on the above data, answer the given subquestions.0.5 marks
19. Q22Based on the above data, answer the given subquestions.0.5 marks
20. Q23Based on the above data, answer the given subquestions.1 mark
21. Q24Based on the above data, answer the given subquestions.1 mark
22. Q25Number of Parameters:_2 marks
23. Q26Computational Cost:2 marks
24. Q27Number of parameters for depthwise seperable convolution:1 mark
25. Q28Computational Cost for depthwise seperable convolution:1 mark
26. Q29Number of parameters for pointwise convolution:_1 mark
27. Q30Computational cost for pointwise convolution:1 mark
28. Q31Number of weights in Weight Matrix U1 is:_1 mark
29. Q32Number of weights in Weight Matrix V1 is:_1 mark
30. Q33Number of weights in Weight Matrix U2 is:_1 mark
31. Q34Number of weights in Weight Matrix V2 is:_0.5 marks
32. Q35Number of weights in Weight Matrix W is:_0.5 marks
33. Q36You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
34. Q37You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
35. Q38You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
36. Q39You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
37. Q40You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
38. Q41You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
39. Q42You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
40. Q43You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
41. Q44You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
42. Q45You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
43. Q46You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
44. Q47You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
45. Q48You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
46. Q49You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
47. Q50You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks
48. Q51You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hi…0.5 marks