Computer Vision in AI: Understanding Image Recognition and Algorithms MCQs
Questions: 30
Questions
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1. What is the primary goal of computer vision in AI?
- a) To generate synthetic images
- b) To enable machines to interpret and understand visual data
- c) To replace all human vision capabilities
- d) To enhance audio recognition models
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2. Which of the following is a common application of computer vision?
- a) Speech recognition
- b) Optical Character Recognition (OCR)
- c) Text summarization
- d) Machine translation
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3. What is the purpose of image segmentation in computer vision?
- a) To split an image into meaningful parts for analysis
- b) To blur an image for artistic effects
- c) To reduce the size of an image
- d) To convert an image into text
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4. Which algorithm is commonly used for face detection?
- a) R-CNN
- b) YOLO (You Only Look Once)
- c) Naïve Bayes
- d) K-Means Clustering
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5. What type of neural network is most commonly used in computer vision tasks?
- a) Recurrent Neural Network (RNN)
- b) Generative Adversarial Network (GAN)
- c) Bayesian Network
- d) Convolutional Neural Network (CNN)
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6. Which dataset is widely used for training image classification models?
- a) MNIST
- b) IMDB Reviews
- c) ImageNet
- d) COCO
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7. What is an activation function commonly used in deep learning for image processing?
- a) Softmax
- b) ReLU (Rectified Linear Unit)
- c) Sigmoid
- d) Tanh
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8. What is the main advantage of YOLO over R-CNN?
- a) YOLO is significantly faster in object detection
- b) YOLO provides higher accuracy in text recognition
- c) YOLO does not require a trained model
- d) YOLO works only on grayscale images
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9. What technique is used to artificially increase the size of a dataset in image recognition?
- a) Dropout
- b) Data Augmentation
- c) Batch Normalization
- d) Gradient Descent
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10. What is Transfer Learning in computer vision?
- a) Using pre-trained models to solve new tasks with minimal training
- b) Learning without labeled data
- c) A method of compressing image data
- d) A way to improve audio processing models
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11. What is the role of edge detection in image processing?
- a) To detect the boundaries of objects within an image
- b) To enhance image resolution
- c) To change the color scheme of an image
- d) To compress image data
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12. Which of the following is NOT an image classification architecture?
- a) ResNet
- b) VGGNet
- c) LeNet
- d) Word2Vec
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13. What does COCO dataset stand for?
- a) Common Objects in Context
- b) Computer Object Classification and Optimization
- c) Convolutional Object Categorization Output
- d) Complex Overlapping Computer Objects
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14. What is the main challenge in object detection?
- a) Recognizing multiple objects in different scales and orientations
- b) Understanding spoken language
- c) Translating text to different languages
- d) Storing high-resolution images
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15. What type of problem does Optical Character Recognition (OCR) solve?
- a) Speech-to-text conversion
- b) Identifying text within images
- c) Image compression
- d) Face recognition
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16. Which of these architectures is commonly used for real-time object detection?
- a) Fast R-CNN
- b) YOLO
- c) AlexNet
- d) GPT-3
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17. What is the term for generating images using AI models?
- a) Image Synthesis
- b) Optical Recognition
- c) Image Compression
- d) Image Clustering
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18. How does CNN differ from traditional neural networks for images?
- a) CNN has no hidden layers
- b) CNN does not require training data
- c) CNN is used only for text processing
- d) CNN uses convolutional layers to extract spatial features
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19. What is the function of Batch Normalization in deep learning?
- a) It speeds up training and stabilizes the learning process
- b) It reduces the number of parameters in a model
- c) It increases the dataset size
- d) It converts an image into text
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20. Which deep learning framework is commonly used for computer vision?
- a) TensorFlow
- b) PyTorch
- c) OpenCV
- d) All of the above
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21. What is the main advantage of ResNet architecture?
- a) It solves the vanishing gradient problem using residual connections
- b) It reduces model training time to zero
- c) It does not use convolutional layers
- d) It is only used for NLP
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22. What is a bounding box in object detection?
- a) A file format for storing images
- b) A method of encrypting images
- c) A rectangular box that encloses an object in an image
- d) A way to remove background noise
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23. What is a heatmap in computer vision?
- a) A visual representation of areas of interest in an image
- b) A temperature measurement tool
- c) A method of increasing image brightness
- d) A way to classify grayscale images
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24. Which of these is a loss function commonly used in image classification?
- a) Mean Squared Error
- b) Cross-Entropy Loss
- c) BLEU Score
- d) Jaccard Index
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25. What is the purpose of feature extraction in image recognition?
- a) To remove noise from an image
- b) To change the resolution of an image
- c) To store images more efficiently
- d) To identify and represent important patterns in an image
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26. What is the role of a fully connected layer in a CNN?
- a) To apply convolution operations to an image
- b) To reduce the size of an image
- c) To map extracted features to the final output classification
- d) To perform edge detection
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27. What is one drawback of deep learning models in image recognition?
- a) They require large amounts of labeled data for training
- b) They cannot process grayscale images
- c) They always misclassify objects
- d) They do not support real-time processing
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28. Which of these is an advanced architecture for handling sequential image data?
- a) LSTM (Long Short-Term Memory)
- b) CNN (Convolutional Neural Network)
- c) GAN (Generative Adversarial Network)
- d) R-CNN (Region-Based Convolutional Neural Network)
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29. What does the term "overfitting" mean in computer vision models?
- a) The model performs better on new data than on training data
- b) The model memorizes training data but fails to generalize well to new images
- c) The model does not learn any patterns
- d) The model ignores high-resolution images
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30. What is a key function of GANs in computer vision?
- a) To classify images into categories
- b) To detect objects in real-time
- c) To perform edge detection
- d) To generate realistic synthetic images
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