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DL for CV · Object detection & segmentation

14 questions · 28 marks · 3 papers

Answer key

Indian Institute of Technology Madras

BS Degree Programme · Examination & Study Paper

Deep Learning for Computer Vision

Object detection & segmentation

Official Paper

Examination Instructions & Guidelines

Solve on notebook/paper first before checking solutions.
MCQ: Exactly 1 option is correct. Negative marking applies.
NAT: Numerical response. No negative marking.
Scientific calculator allowed for all calculations.
Official answer key is sealed at the end of this sheet.

End Term · 31 Aug 2025 (FN)End Term

Q1MCQ2 Marks·Choose one option
Key ↓
1. A
2. B
3. C
4. D
Q2MCQ2 Marks·Choose one option
Asked 2 times Key ↓
1. A1→ iv, 2→ v, 3→ i, 4 → ii
2. B1→ iii, 2→ i, 3 → v, 4→ ii
3. C1→ iv, 2→ iii, 3→ ii, 4 → i
4. D1→ iii, 2→ i, 3 → ii, 4→ v
Q3MCQ2 Marks·Choose one option
Asked 2 times Key ↓
What makes the Segment Anything Model (SAM) particularly advantageous for image annotation tasks compared to conventional segmentation approaches?
1. AIt has perfect segmentation accuracy
2. BIt has minimal hardware requirements for operation
3. CIt can generalize to segment objects it wasn’t specifically trained on
4. DIt automatically identifies and labels all objects in an image without user prompts
Q4MCQ2 Marks·Choose one option
Key ↓
Statement 1: The Segment Anything Model (SAM) demonstrates zero-shot generalization capabilities across various downstream tasks. Statement 2: SAM’s training involves a largescale, diverse mask dataset using a prompt-based segmentation approach. Which one of the following options is most appropriate?
1. ABoth statements are correct, and Statement 2 correctly explains Statement 1.
2. BBoth statements are correct, but Statement 2 does not correctly explain Statement 1.
3. CStatement 1 is correct, but Statement 2 is incorrect.
4. DStatement 1 is incorrect, but Statement 2 is correct.
5. EBoth statements are incorrect.
Q5MCQ2 Marks·Choose one option
Asked 2 times Key ↓
Why does DETR typically exhibit poor performance in detecting small objects compared to larger ones?
1. AThe CNN backbone used in DETR has a fixed receptive field that’s too large for small objects
2. BThe global self-attention mechanism in DETR tends to dilute the signal from small objects across all image locations
3. CDETR’s loss function actively discards all detections smaller than 50x50 pixels
4. DThe object queries in DETR are programmed to ignore any object smaller than 10
5. EDETR’s encoder-decoder architecture was specifically designed to only detect large objects

End Term · 31 Aug 2025 (AN)End Term

Q6MCQ2 Marks·Choose one option
Asked 2 times Key ↓
Why does DETR typically exhibit poor performance in detecting small objects compared to larger ones? Choose the best answer in your opinion.
1. AThe CNN backbone used in DETR has a fixed receptive field that’s too large for small objects.
2. BThe global self-attention mechanism in DETR tends to dilute the signal from small objects across all image locations.
3. CDETR’s loss function actively discards all detections smaller than 50 × 50 pixels.
4. DThe object queries in DETR are programmed to ignore any object smaller than 10% of the image size.
5. EDETR’s encoder-decoder architecture was specifically designed to only detect objects larger than a cat.
Q7MCQ2 Marks·Choose one option
Asked 2 times Key ↓
1. A1→ iv, 2→ v, 3→ i, 4 → ii
2. B1→ iii, 2→ i, 3 → v, 4→ ii
3. C1→ iv, 2→ iii, 3→ ii, 4 → i
4. D1→ iii, 2→ i, 3 → ii, 4→ v
Q8MCQ2 Marks·Choose one option
Asked 2 times Key ↓
Why might Segment Anything (SAM) be particularly useful in data annotation tasks compared to traditional segmentation models?
1. AIt produces perfect segmentation masks.
2. BIt can adapt to segment any object, even those not seen during training
3. CIt requires less computational resources
4. DIt automatically labels all objects in an image without user input

Quiz 2 · 3 Aug 2025 (AN)Quiz 2

Q9MCQ2 Marks·Choose one option
Key ↓
Which of the following architectures introduced the ROI pooling layer for extracting fixed-size feature maps from variable-size region proposals?
1. AR-CNN
2. BFast R-CNN
3. CFaster R-CNN
4. DYOLO
Q10MCQ2 Marks·Choose one option
Key ↓
Which object detection method first integrated a Region Proposal Network (RPN) to generate region proposals within the detection model?
1. AR-CNN
2. BFast R-CNN
3. CFaster R-CNN
4. DYOLO
Q11MCQ2 Marks·Choose one option
Key ↓
Which architecture performs object detection as a single regression problem from image pixels to bounding boxes and class probabilities?
1. AR-CNN
2. BFast R-CNN
3. CFaster R-CNN
4. DYOLO
Q12NAT2 Marks·Write the number
Key ↓
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 ____________(in %)
Your answer:
Q13MCQ2 Marks·Choose one option
Key ↓
1. A
2. B
3. C
4. D
Q14NAT2 Marks·Write the number
Key ↓
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 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.
Your answer:

Official Key

Master Answer Key & Explanations

Official answer keys from IIT Madras BS past papers. Click any question to review.

Reveal Key

Q1D

Q2A

Q3C

Q4A

Q5B

Q6B

Q7A

Q8B

Q9B

Q10C

Q11D

Q1228

Q13C

Q14384

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