Image Captioning — MCQs | Digital Image Processing

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1. Which deep learning model is most commonly used for generating image captions?



2. Which dataset is widely used for training image captioning models?



3. In image captioning, the CNN is mainly responsible for:



4. Which neural network component is typically used after CNN in image captioning?



5. The goal of image captioning is to generate:



6. Which architecture improves performance in image captioning by handling long-range dependencies?



7. In attention-based models, attention mechanism helps the model:



8. Which loss function is commonly used for training image captioning models?



9. BLEU score is used in image captioning to measure:



10. The attention mechanism in image captioning was introduced in which model?



11. Image captioning typically combines which two types of data?



12. Which model architecture enables parallel training in caption generation?



13. Which metric evaluates n-gram overlap in caption generation?



14. CIDEr metric in image captioning emphasizes:



15. The encoder in an image captioning model processes:



16. The decoder in image captioning is responsible for:



17. Which optimization algorithm is commonly used in training captioning models?



18. Which of the following is a common challenge in image captioning?



19. In image captioning, what is “teacher forcing”?



20. What does the term “vocabulary” refer to in image captioning?



21. What is beam search used for in caption generation?



22. The term “Show and Tell” refers to:



23. Which layer captures time-dependent patterns in sequence generation?



24. What is the main input to the decoder during testing in image captioning?



25. Which of the following is a pre-trained model commonly used for feature extraction in image captioning?



26. What does the “context vector” in attention models represent?



27. Why is dropout used in image captioning models?



28. Which method improves robustness of captioning models?



29. Which of the following is a captioning benchmark dataset?



30. What role does a tokenizer play in image captioning?



31. Which deep learning technique allows for generating varied captions for the same image?



32. What is “caption diversity” in image captioning?



33. Which transformer-based model is adapted for image captioning?



34. What does the term “visual grounding” mean in captioning?



35. Which of the following can improve caption fluency?



36. Which evaluation metric accounts for semantic similarity in captions?



37. Which of the following does not belong to image captioning evaluation metrics?



38. In a captioning model, which layer is most likely used at the end of decoder?



39. Which word usually marks the start of a generated caption sequence?

41. Which of these is used for fine-tuning captions after generation?



42. Which part of the image is mostly used in spatial attention?



43. Why are hierarchical models used in captioning?



44. In self-critical sequence training (SCST), reward is computed using:



45. Which of the following helps model rare words in captions?



46. Which method avoids repetition in captioning outputs?



47. What is a major limitation of greedy decoding?



48. How is the quality of generated captions usually assessed?



49. Which component in an image captioning model interprets visual data into a fixed-size representation?



50. Which of the following best describes a key advantage of using Transformers in image captioning?



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