700+ Gen AI For Computer Vision Interview Questions (MAANG)
Ace Computer Vision Interviews with 500+ MCQs and Expert Explanations
700+ Gen AI For Computer Vision Interview Questions (MAANG) free download
Ace Computer Vision Interviews with 500+ MCQs and Expert Explanations
This course provides a comprehensive set of 700+ multiple-choice questions designed to test and sharpen your knowledge in computer vision. Covering essential topics like image processing, convolutional neural networks (CNNs), object detection, segmentation, and feature extraction, this course helps you prepare for real-world technical interviews. Each question includes a detailed explanation to enhance understanding and reinforce core concepts. Whether you're interviewing for an AI role or brushing up on your computer vision skills, this course is your go-to resource.
I. Core Computer Vision Fundamentals (Difficulty: Easy to Medium)
MCQ Questions: 100
Image Representation and Basics:
Detailed Topics: Pixels, image formats (RGB, Grayscale, HSV), image channels, resolution, aspect ratio.
Subtopics: Digital image formation, color spaces and their conversions, image quantization.
Image Processing Techniques:
Detailed Topics:
Filtering: Convolution, kernels (Gaussian, Sobel, Laplacian), blurring, sharpening, edge detection (Sobel, Canny).
Morphological Operations: Erosion, dilation, opening, closing.
Image Transformations: Resizing, rotation, translation, cropping, affine transformations, perspective transformations.
Feature Extraction (Traditional): SIFT, SURF, ORB, HOG. (Understand the core idea, not necessarily implementation details).
Subtopics: Noise reduction techniques, histogram equalization, image pyramids, feature descriptors vs. detectors.
Basic Computer Vision Tasks:
Detailed Topics:
Image Classification: What it is, basic approaches (e.g., k-NN on image features).
Object Detection (Traditional): Sliding window approaches, Viola-Jones (face detection).
Image Segmentation (Basic): Thresholding, connected components.
Subtopics: Evaluation metrics for classification (accuracy, precision, recall, F1-score), IoU for detection.
II. Deep Learning Models in Computer Vision (Difficulty: Medium to Hard)
MCQ Questions: 250
Neural Network Fundamentals:
Detailed Topics: Neurons, activation functions (ReLU, Sigmoid, Tanh, Softmax), feed-forward networks, backpropagation, gradient descent (variants: SGD, Adam, RMSProp), loss functions (MSE, Cross-Entropy).
Subtopics: Vanishing/exploding gradients, learning rate schedules, batch normalization, regularization (L1, L2, Dropout).
Convolutional Neural Networks (CNNs):
Detailed Topics:
Convolutional Layer: Filters/kernels, stride, padding, receptive field.
Pooling Layers: Max pooling, average pooling.
Architectures: LeNet, AlexNet, VGG, ResNet (residual connections), Inception (multi-scale processing), DenseNet.
Understanding specific components: Batch Normalization, Group Normalization, Weight Initialization (Xavier, He).
Subtopics: Advantages of CNNs over fully connected networks for images, parameter efficiency, understanding feature hierarchies.
Advanced Deep Learning Concepts:
Detailed Topics:
Transfer Learning: Fine-tuning, feature extraction, pre-trained models.
Data Augmentation: Common techniques (flips, rotations, scaling, cropping, color jitter).
Optimization Strategies: Advanced optimizers (AdamW), learning rate schedulers (cosine annealing, cyclical learning rates).
Regularization Techniques: Dropout, Batch Normalization, Layer Normalization, Group Normalization.
Subtopics: Strategies for handling imbalanced datasets, understanding overfitting and underfitting.
Deep Learning for Computer Vision Tasks:
Detailed Topics:
Image Classification: Architectures for classification, metrics, transfer learning applications.
Object Detection:
Two-stage detectors: R-CNN, Fast R-CNN, Faster R-CNN (Region Proposal Network - RPN).
One-stage detectors: YOLO (all versions: v1, v2, v3, v4, v5, v7, v8), SSD.
Concepts: Anchor boxes, NMS (Non-Maximum Suppression), RoI Pooling/Align.
Image Segmentation:
Semantic Segmentation: FCN, U-Net, DeepLab.
Instance Segmentation: Mask R-CNN.
Concepts: Pixel-wise classification, segmentation metrics (IoU, Dice Coefficient).
Pose Estimation: OpenPose, AlphaPose (high-level understanding).
Image Generation (Foundational): Autoencoders (VAE), GANs (basic concepts, generator, discriminator, adversarial loss, mode collapse).
Subtopics: Loss functions for object detection (e.g., Focal Loss), trade-offs between speed and accuracy for different detection models, applications of each task.
III. Transformers in Computer Vision (Difficulty: Hard)
MCQ Questions: 150
Transformer Architecture Fundamentals:
Detailed Topics:
Self-Attention Mechanism: Query, Key, Value vectors, scaled dot-product attention, attention scores, attention matrix.
Multi-Head Attention: Advantages, how it works.
Positional Encoding: Why it's needed in vision transformers, different types (learnable, sinusoidal).
Encoder-Decoder Architecture (General): How transformers are used in both encoder-only (BERT-like) and encoder-decoder (Seq2Seq) setups.
Feed-Forward Networks (FFN): Role within the transformer block.
Layer Normalization and Residual Connections: Their importance in stabilizing training.
Subtopics: Computational complexity of attention, comparison to RNNs and CNNs for long-range dependencies.
Vision Transformers (ViT):
Detailed Topics:
Patching: How images are divided into patches.
Linear Embedding: Converting patches into linear sequences.
Class Token: Its role in classification.
Training Strategy: Pre-training on large datasets, fine-tuning.
Comparison to CNNs: Strengths (global context, scalability) and weaknesses (data hungry, inductive biases).
Subtopics: Variants of ViT (e.g., DeiT, Swin Transformer), efficiency improvements in ViTs.
Applications of Transformers in Computer Vision:
Detailed Topics:
Image Classification: ViT for classification.
Object Detection: DETR (DEtection TRansformer), MaskFormer, Perceiver IO. Understanding the end-to-end nature and removal of NMS in DETR.
Image Segmentation: Segmentation with ViTs (e.g., using ViT backbones with U-Net decoders), MaskFormer for panoptic segmentation.
Image Generation: Generative Adversarial Transformers (GATs), diffusion models with Transformer backbones (e.g., in stable diffusion).
Subtopics: Advantages of transformer-based models for certain CV tasks (e.g., long-range dependencies, end-to-end learning).
Challenges and Future Directions:
Detailed Topics: Computational cost of transformers, data hunger, interpretability of attention maps, integrating inductive biases.
Subtopics: Hybrid CNN-Transformer architectures, sparse attention mechanisms.
IV. Generative AI for Computer Vision (Deep Dive) (Difficulty: Hard)
MCQ Questions: 150
Generative Adversarial Networks (GANs):
Detailed Topics:
Core Principle: Generator vs. Discriminator, minimax game, adversarial loss.
Training Challenges: Mode collapse, training instability, vanishing gradients (for generator), oscillations.
Architectures: DCGAN (Deep Convolutional GANs), Conditional GANs (cGANs), WGAN (Wasserstein GAN), WGAN-GP (Gradient Penalty).
Evaluation Metrics: Inception Score (IS), Frechet Inception Distance (FID).
Applications: Image synthesis, super-resolution, inpainting, image-to-image translation (CycleGAN, Pix2Pix).
Subtopics: Understanding the role of different loss functions (e.g., least squares GAN), spectral normalization, BigGAN.
Variational Autoencoders (VAEs):
Detailed Topics:
Core Principle: Encoder, Decoder, Latent Space, Reparameterization Trick.
Loss Function: Reconstruction loss + KL divergence.
Properties: Smooth latent space, probabilistic generation.
Comparison to GANs: Strengths (better latent space control, less mode collapse) and weaknesses (blurrier samples).
Applications: Image generation, anomaly detection, dimensionality reduction.
Subtopics: Different types of VAEs (e.g., β-VAE), disentangled representations.
Diffusion Models (Denoising Diffusion Probabilistic Models - DDPMs):
Detailed Topics:
Core Principle: Forward diffusion process (adding noise), reverse denoising process (learning to remove noise).
Training: Learning a noise predictor network (often U-Net based).
Sampling: Iterative denoising from pure noise.
Advantages: High-quality image generation, mode coverage, stability compared to GANs.
Conditional Generation: Text-to-Image (Stable Diffusion, DALL-E 2 high-level architecture), Class-Conditional generation.
Subtopics: Classifier Guidance, Classifier-Free Guidance, Latent Diffusion Models (LDMs), different types of noise schedules. This is a rapidly evolving field, so a good understanding of recent advancements is beneficial.
Autoregressive Models (for pixels, high-level):
Detailed Topics: PixelRNN, PixelCNN. (Understand the concept of generating pixels sequentially, limitations for high-res images).
Subtopics: Masked convolutions in PixelCNN.
Applications of Generative AI in CV:
Detailed Topics:
Image Synthesis and Editing: High-resolution image generation, style transfer, image inpainting, outpainting.
Data Augmentation: Generating synthetic training data to improve model robustness.
Super-Resolution: Enhancing image resolution.
Novel View Synthesis: Generating new views of a scene (e.g., NeRF - high-level understanding).
Adversarial Attacks and Defenses: Understanding how generative models can be used to create adversarial examples or robust models.
Subtopics: Ethical considerations in generative AI (deepfakes, bias).
Evaluation Metrics for Generative Models:
Detailed Topics: Inception Score (IS), Fréchet Inception Distance (FID), Perceptual Quality (human evaluation).
Subtopics: Limitations of these metrics, how they are calculated.
V. General Deep Learning & AI Concepts (Difficulty: Easy to Medium)
MCQ Questions: 50
Machine Learning Basics: Supervised, Unsupervised, Reinforcement Learning.
Model Evaluation: Bias-Variance Tradeoff, Confusion Matrix, ROC curves, AUC.
Hyperparameter Tuning: Grid Search, Random Search, Bayesian Optimization.
Deployment Considerations: Model compression (pruning, quantization), inference speed, hardware considerations (GPUs, TPUs).
Ethical AI: Bias in datasets, fairness, explainability (XAI concepts like LIME, SHAP - high-level).
