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DL for CV · End Term · 31 Aug 2025 (Afternoon)

49 questions · 72 marks · 90 min

Answer key Take as mock

Indian Institute of Technology Madras

BS Degree Programme · Examination & Study Paper

Deep Learning for Computer Vision

End Term · 31 Aug 2025 (Afternoon)

Official Paper

Examination Instructions & Guidelines

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

Q1MCQ2 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.
Q2MCQ2 Marks·Choose one option
Key ↓
Consider the following Pytorch code: 1. x = torch.tensor([[-3.,2.,3.],[4.,-5.,7.],[20.,3.,2.]]) 2. print(torch.mean(x,dim=0,keepdim=True)) The output of the code is:
1. Atensor([ 7., 0., 4.])
2. Btensor([[ -7., 10., -4.]])
3. Ctensor([[ -7.],[7.],[4.]])
4. Dtensor([ -7., 7., 3.])
5. Etensor([[7., 0., 4.]])
6. Ftensor([[ 0.],[7.],[4.]])
Q3MCQ2 Marks·Choose one option
Key ↓
Which one of the following statements is false?
1. ALinear contrast stretching is a point operation.
2. BMoving average is an example of a local operation.
3. CConvolution in the spatial domain cannot be obtained through addition in the frequency domain.
4. DAll of these.
Q4MCQ2 Marks·Choose one option
Key ↓
What is the correct order of operations for processing an image through a Vision Transformer (ViT)?
1. AImage patching → Positional embedding → Linear projection of flattened patches → Transformer encoder→ Classification head
2. BImage patching → Linear projection of flattened patches → Positional embedding → Transformer encoder→ Classification head
3. CPositional embedding → Image patching → Linear projection of flattened patches → Transformer encoder→ Classification head
4. DLinear projection of Images → Image patching → Positional embedding → Transformer encoder → Classification head
5. ELinear projection of Images → Image patching → Positional embedding → Transformer encoder → Transformer decoder→ Classification head
Q5MCQ2 Marks·Choose one option
Key ↓
Vector Quantized Variational Autoencoder (VQ-VAE) utilizes a discrete latent representation as opposed to continuous latent spaces used in traditional VAEs. One of the key components of a VQ- VAE is the codebook, which consists of a set of learnable vectors. What is the primary role of the codebook in VQ-VAE?
1. AIt performs the non-linear transformation of the input data to a higher dimensional space.
2. BIt regularizes the encoder by penalizing complex encodings.
3. CIt provides a finite set of vectors which the encoder’s outputs are mapped to, effectively quantizing the latent space.
4. DIt decodes the quantized vectors back into the reconstructed input space.
Q6MCQ2 Marks·Choose one option
Key ↓
Given two normalized embeddings from CLIP, I = [0.2, 0.5, 0.3, 0.4] for an image and T = [0.1, 0.6, 0.3, 0.7] for a text description, compute the CLIPScore(I,T).
1. A− 69%
2. B0%
3. C69%
4. D50%
Q7MCQ2 Marks·Choose one option
Asked 2 times Key ↓
Which one of the following statements regarding hyperparameter tuning is false?
1. AValidation set is the best choice for tuning hyperparameters
2. BPicking hyperparameters that perform the best on the training set leads to overfitting
3. CTuning hyperparameters on the test set is an acceptable approach to find the most powerful model for generalization performance
4. DTest set performance, with hyperparameters tuned on the validation set, would be a realistic measure of how the model will generalize to new data.
Q8MCQ2 Marks·Choose one option
Asked 4 times Key ↓
Which one of the following statements is false? (Pick the most appropriate one.)
1. AAttention mechanisms can be applied to the bidirectional RNN model
2. BAn image captioning network can be trained end-to-end even though we are using 2 different modalities to train the network
3. COne of the key components in the vanilla transformer is the recurrent connection that help them to deal with variable input length.
4. DAll of these.
Q9MCQ2 Marks·Choose one option
Asked 3 times Key ↓
You are designing an edge detection pipeline for noisy grayscale images. For each of the following goals, choose the most appropriate filter or method: 1. Smooth out noise before any edge detection step 2. Detect edges by finding zero-crossings in the second derivative 3. Highlight regions with a strong intensity gradient 4. Perform basic edge detection with a simple directional gradient filter
1. A1 → Gaussian filter, 2 → Laplacian of Gaussian, 3 → First derivative of Gaussian, 4 → Sobel operator
2. B1 → Sobel operator, 2 → Gaussian filter, 3 → Laplacian of Gaussian, 4 → First derivative of Gaussian
3. C1 → First derivative of Gaussian, 2 → Sobel operator, 3 → Gaussian filter, 4 → Laplacian of Gaussian
4. D1→Laplacian of Gaussian, 2→First derivative of Gaussian, 3→Sobel operator, 4→Gaussian filter
Q10MCQ2 Marks·Choose one option
Asked 3 times Key ↓
1. A
2. B
3. C
4. D
Q11MCQ2 Marks·Choose one option
Asked 2 times Key ↓
1. A1→ iii, 2→ iv, 3→ ii, 4 → i
2. B1→ i, 2→ iv, 3 → iii, 4→ ii
3. C1→ iv, 2→ iii, 3→ ii, 4 → i
4. D1→ iii, 2→ i, 3 → ii, 4 →v
Q12MCQ2 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
Q13MCQ2 Marks·Choose one option
Asked 2 times Key ↓
In which one of the following applications would you use a many- to-one RNN architecture? Choose the most appropriate answer.
1. AInput is a review of a movie and output is the sentiment of the given review
2. BInput is an image and output is the textual description of the given image
3. CInput is an image and output is the class to which the given image belongs
4. DInput is a sentence in English language and output is the same sentence translated to Hindi language
Q14MCQ2 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
Q15MSQ2 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following statements are true? (Select all possible correct options)
1. AVariational AutoEncoders (VAE) generate sharper images compared to Generative adversarial networks (GANs)
2. BGAN is an example of an implicit density estimation model
3. CFully connected layers in mapping network of Style-GAN change the dimension of its input
4. DIn a GAN, the generator and discriminator are trained in an alternating fashion, each using the other’s output to improve.
Q16MSQ2 Marks·Choose all correct options
Key ↓
Classifier-free guidance is a technique used in diffusion models to improve sample quality without the explicit use of a classifier. This technique involves modifying the sampling process based on a control parameter. If the control parameter, denoted as γ, is set to zero, what effect does this have on the generation process?
1. AIt fails to generate realistic samples
2. BIt removes all guidance, effectively making the process equivalent to the unconditional generation.
3. CIt maximizes the influence of the classifier, leading to highly detailed generations.
4. DIt can lead to more diverse samples compared to higher values of γ, as the generation process is less constrained by the conditional information.
Q17MSQ2 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following statements are true? (Select all possible correct options)
1. AAutoencoder are equivalent to Principal Component Analysis (PCA) provided we don’t use of non-linear activation functions
2. BWhen using global attention on temporal data, alignment weights are learnt for encoder hidden representations for all time steps
3. CPositional encoding is an important component of the trans- former architecture as it conveys information about order in a given sequence
4. DIt is not possible to generate different captions for the same image that have similar meaning but different tone/style
5. EAutoencoders can not be used for data compression as its input and output dimensions are different
Q18MSQ2 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following statements about CLIP are TRUE? (Select ALL that apply)
1. ACLIP uses a cross-entropy loss function to align text and image embeddings.
2. BCLIP requires image labels for training.
3. CCLIP is trained to maximize cosine similarity between embeddings of matching text-image pairs.
4. DCLIP model is trained with pretext tasks such as rotation prediction and inpainting to learn effective feature representations.
5. ETo use the CLIP model for image classification, it is imperative to finetune it on downstream datasets.
Q19MSQ2 Marks·Choose all correct options
Asked 2 times Key ↓
Which one of the following statements is true:
1. AWeight change criterion is a method of ‘early stopping’ that checks whether or not the error is dropping over epochs to decide whether to continue training or stop.
2. BL1 norm tends to create more sparse weights than L2 norm.
3. CDuring the training phase, for each iteration, Dropout ignores a random fraction, p, of nodes, and accounts for it in the test phase by scaling down the activations by a factor of p.
4. DA single McCulloch-Pitts neuron is capable of modeling AND, OR, XOR, NOR, and NAND functions.
Q20MSQ2 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following are examples of a high-pass filter?
1. A
2. B
3. C
4. D
Q21NAT2 Marks·Write the number
Asked 3 times Key ↓
Your answer:
Q22NAT2 Marks·Write the number
Asked 3 times Key ↓
Your answer:
Q23NAT2 Marks·Write the number
Asked 2 times Key ↓
Your answer:
Q24NAT2 Marks·Write the number
Asked 2 times Key ↓
Your answer:
Q25NAT2 Marks·Write the number
Asked 2 times Key ↓
Your answer:
Q26NAT2 Marks·Write the number
Asked 3 times Key ↓
What is the size of the feature map after applying two successive convolution operations with given parameters? Image size = 72×72, Kernel size = 5×5, Padding = 2 and Stride = 3. (In calculation, take floor(x) whenever x is non-integer.If the answer is N × N, write just one value N in the blank) __________________ .
Your answer:
Read the passage below, then answer Q27–Q28
Based on the above data, answer the given subquestions.
Q27NAT1 Mark·Write the number
Asked 4 times Key ↓
Your answer:
Q28NAT1 Mark·Write the number
Asked 4 times Key ↓
Your answer:
Read the passage below, then answer Q29–Q32
Based on the above data, answer the given subquestions.
Q29NAT2 Marks·Write the number
Key ↓
Element 1:___________
Your answer:
Q30NAT2 Marks·Write the number
Key ↓
Element 2:___________
Your answer:
Q31NAT1 Mark·Write the number
Key ↓
Element 3:___________
Your answer:
Q32NAT1 Mark·Write the number
Key ↓
Element 4:___________
Your answer:
Read the passage below, then answer Q33–Q43
Consider the BLIP model architecture with an input (image-text pair) as shown below:
For each of the entities given, enter the appropriate number from the figure that corresponds to it:
Based on the above data, answer the given subquestions.
Q33NAT1 Mark·Write the number
Asked 22 times Key ↓
Self-Attention _______________
Your answer:
Q34NAT0.5 Marks·Write the number
Asked 22 times Key ↓
BERT Text Encoder Bi Self Attention:__________________
Your answer:
Q35NAT0.5 Marks·Write the number
Asked 22 times Key ↓
Image-Text Matching Loss (LITM ):__________________
Your answer:
Q36NAT0.5 Marks·Write the number
Asked 22 times Key ↓
Image Grounded Text Encoder Bi Self Attention: ___________________
Your answer:
Q37NAT0.5 Marks·Write the number
Asked 22 times Key ↓
Your answer:
Q38NAT0.5 Marks·Write the number
Asked 22 times Key ↓
Image Grounded Text Encoder Cross Attention: _________________
Your answer:
Q39NAT0.5 Marks·Write the number
Asked 22 times Key ↓
Image-Text Contrastive Loss (LITC): ____________
Your answer:
Q40NAT0.5 Marks·Write the number
Asked 22 times Key ↓
Your answer:
Q41NAT0.5 Marks·Write the number
Asked 22 times Key ↓
Causal Self Attention: _____________
Your answer:
Q42NAT0.5 Marks·Write the number
Asked 22 times Key ↓
Your answer:
Q43NAT0.5 Marks·Write the number
Asked 22 times Key ↓
Image Grounded Text Decoder Cross Attention: _____________
Your answer:
Read the passage below, then answer Q44–Q49
Based on the above data, answer the given subquestions.
Q44NAT1 Mark·Write the number
Asked 13 times Key ↓
Sigmoid ______________
Your answer:
Q45NAT1 Mark·Write the number
Asked 13 times Key ↓
Linear ______________
Your answer:
Q46NAT1 Mark·Write the number
Asked 13 times Key ↓
Indicator Function ______________
Your answer:
Q47NAT1 Mark·Write the number
Asked 13 times Key ↓
Softplus _____________
Your answer:
Q48NAT1 Mark·Write the number
Asked 13 times Key ↓
ReLU _______________
Your answer:
Q49NAT1 Mark·Write the number
Asked 13 times Key ↓
Leaky-ReLU _____________
Your answer:

Official Key

Master Answer Key & Explanations

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

Reveal Key

Q1B

Q2E

Q3D

Q4B

Q5C

Q6C

Q7C

Q8C

Q9A

Q10A

Q11D

Q12A

Q13A

Q14B

Q15B, D

Q16B, D

Q17A, B, C

Q18C

Q19B, C

Q20A, B, C

Q2193 to 94

Q220.93 to 0.94

Q2343

Q243.75

Q252

Q268

Q27-2

Q285

Q290.49

Q300.55

Q310.1

Q320.5

Q331

Q345

Q3511

Q367

Q378

Q384

Q3910

Q409

Q413

Q422

Q436

Q440.99

Q4526.01

Q461

Q4726.01

Q4826.01

Q4926.01

Questions and answer keys are reproduced from the official IIT Madras BS papers. BTechi is not affiliated with IIT Madras.