Quiz Space

Deep Learning for Computer Vision · Quiz 2 · 12 Apr 2026 · January 2026 term

Deep Learning for Computer Vision Quiz 2 12 Apr 2026 — Question 11

Question 11

+1 markOne correct option

RetinaNet is notable primarily because it introduced:

  1. A

    A novel feature pyramid network (FPN) architecture that eliminates the need for anchor boxes

  2. B

    Focal loss to address class imbalance

  3. C

    The concept of Region of Interest (RoI) pooling to align features extracted from different scales

  4. D

    A dynamic routing mechanism that directly assigns bounding box predictions to ground truth objects

Show answer

Correct answer

  • B

    Focal loss to address class imbalance

Video solution

The video solution to this question is being recorded and will play here. Until then, the answer key is above.

See the whole paperTake it as a mock test

Question 11 of 23 in the IIT Madras BS Deep Learning for Computer Vision (Deep Learning for Computer Vision) Quiz 2 paper sat on 12 Apr 2026, in the January 2026 term (Deep Learning For Computer Vision 07 Apr 26). It carries 1 mark.

More questions from this paper

  1. Q1Why do very deep plain CNNs (without skip connections) sometimes show higher training error than shallower CNNs?
  2. Q2In a standard ResNet-50 bottleneck block, the three convolutions are typically:
  3. Q3In Inception/GoogLeNet modules, the main purpose of 1×1 convolutions is to:
  4. Q4MobileNetV1 reduces computation primarily by using:
  5. Q5EfficientNet’s key scaling idea is:
  6. Q6You have a pretrained CNN backbone and only 500 labeled images for a new task. A strong first baseline is:
  7. Q7When fine-tuning a model, what is a common way to prevent the new training from "overwriting" or destroying the useful …
  8. Q8Which method produces a heatmap by weighting convolutional feature maps using gradients of a target class score?
  9. Q9Which detector is a classic two-stage detector with an RPN + ROI feature extraction?
  10. Q10Which statement is most accurate?
  11. Q12A model that predicts a class label for every pixel without separating object instances is:
  12. Q13Vanishing gradients in vanilla RNNs are largely caused by:
  13. Q14Which statement is true?
  14. Q15Which statements can be true in practice when fine-tuning with small batch sizes?
  15. Q16Which statements are true?
  16. Q17Which statements are true ?
  17. Q18Input feature map: 14×14×128, output: 14×14×256, kernel: 3×3, stride 1, same padding, ignore bias. Compute number of pa…
  18. Q19Using the same setup (Input feature map: 14×14×128, output: 14×14×256, kernel: 3×3, stride 1, same padding, ignore bias…
  19. Q20A ResNet bottleneck block takes 256 channels in and uses: 1×1 conv to 256 channels → 3×3 conv at 256 → 1×1 conv to 1024…
  20. Q21A detector uses three pyramid levels: P3 = 80×80, P4 = 40×40, P5 = 20×20. At each location, it uses 9 anchors (3 scales…
  21. Q22Box A: top-left (2,2), bottom-right (10,12) Box B: top-left (6,5), bottom-right (14,15) Compute IoU rounded to 3 decima…
  22. Q23Let query q = [1,2]T and keys: k1 = [1,0]T, k2 = [0,1]T, k3 = [1,1]T. Scores si = qT ki. Use softmax over scores. Use e…