Semantic & Instance Segmentation (Mask R-CNN, U-Net) — MCQs | Digital Image Processing

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1. What is the primary goal of semantic segmentation in image processing?



2. Which segmentation technique assigns different labels to distinct object instances of the same class?



3. Which deep learning model is widely used for instance segmentation?



4. Which architecture is commonly used for biomedical image segmentation tasks?



5. In Mask R-CNN, what component is responsible for predicting object masks?



6. What does U-Net use to combine low-level and high-level features?



7. Which part of U-Net architecture is responsible for upsampling the feature maps?



8. What is ROIAlign used for in Mask R-CNN?



9. Which loss function is commonly used for pixel-wise classification in segmentation?



10. What is a major limitation of semantic segmentation?



11. What advantage does instance segmentation provide over semantic segmentation?



12. Which layer in U-Net is responsible for downsampling the input image?



13. In semantic segmentation, which class is often used to represent background pixels?



14. What is the output size of a segmentation model for an image of size 256×256?



15. Why is skip connection used in U-Net?



16. Which component in Mask R-CNN is modified from Faster R-CNN to enable segmentation?



17. What type of data is required to train a semantic segmentation model?



18. What does the term “end-to-end” mean in the context of segmentation models?



19. Which evaluation metric is commonly used for segmentation tasks?



20. What is the role of data augmentation in training segmentation models?



21. What kind of masks does Mask R-CNN generate for detected objects?



22. Which model extends Faster R-CNN for segmentation tasks?



23. What does the “contracting path” in U-Net primarily consist of?



24. What kind of convolution is used to upsample feature maps in U-Net?



25. Which activation function is typically used at the final layer of a binary segmentation model?



26. Which activation function is used at the final layer of a multi-class semantic segmentation model?



27. What problem does semantic segmentation aim to solve in image processing?



28. In segmentation, what is the meaning of class imbalance?



29. Which of the following models is NOT primarily used for segmentation?



30. Which backbone is commonly used in Mask R-CNN?



31. How does Mask R-CNN improve spatial alignment compared to Faster R-CNN?



32. Which model uses a U-shaped architecture for segmentation?



33. What is one limitation of U-Net?



34. In segmentation, what is meant by a “mask”?



35. Which layer increases the spatial resolution of features in U-Net?



36. Which problem arises due to overlapping objects in instance segmentation?



37. How is multi-class segmentation different from binary segmentation?



38. What does a pixel-wise classification involve?



39. Which of the following is a challenge in semantic segmentation?



40. What does “fine-grained segmentation” refer to?



41. What is the function of the decoder in U-Net?



42. Which image modality is U-Net particularly effective for?



43. What is the role of batch normalization in segmentation networks?



44. Which type of data labeling is required for training Mask R-CNN?



45. Which model applies atrous (dilated) convolution for segmentation tasks?



46. Why is semantic segmentation important in autonomous driving?



47. What is one benefit of using data augmentation in segmentation tasks?



48. What is an important property of the masks predicted by Mask R-CNN?



49. What is the main purpose of the Region Proposal Network (RPN) in Mask R-CNN?



50. Which challenge is commonly addressed using post-processing in segmentation models?



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