AI-102 Implement computer vision solutions Practice Question
A company uses the Computer Vision Image Analysis API to generate captions for images. The captions are often too generic. How can they improve the descriptiveness of captions?
⚠ Common exam trap
Many candidates confuse increasing the confidence threshold with improving descriptiveness, when in reality it only reduces the number of captions returned without adding detail.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the Dense Captioning feature.
The Dense Captioning feature in the Computer Vision Image Analysis API generates one-sentence descriptions for each of up to 10 regions detected in an image, providing more specific and detailed captions than the single generic caption. This directly addresses the problem of captions being too generic by breaking the image into meaningful areas and describing each one individually.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the Object Detection API instead.
Why it's wrong here
Object detection returns bounding boxes and tags, not descriptions.
- ✗
Train a custom model with Custom Vision.
Why it's wrong here
Custom Vision does not support caption generation natively.
- ✗
Increase the confidence threshold for captions.
Why it's wrong here
Higher threshold may reduce false positives but does not add detail.
- ✓
Use the Dense Captioning feature.
Why this is correct
Dense captioning generates more detailed descriptions for regions of the image.
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Senior Network & Security Engineer · founder of Courseiva
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