Open-Vocabulary Object Detection Quiz

Answers and explanations appear as you take the quiz.

1. What is the defining capability of open-vocabulary object detection?
   • Detecting only the categories seen with boxes during training
   • Running faster than all other detectors
   • Detecting objects described by arbitrary text, including unseen categories
   • Working only on grayscale images
2. In a common embedding-based open-vocabulary detector, how are candidate regions matched to user-supplied labels?
   • By using only the region's average brightness
   • By comparing visual-region features with text embeddings in a compatible representation space
   • By choosing labels alphabetically without visual features
   • By restricting every model to the original COCO class list
3. What pretraining resource most enables open-vocabulary detection?
   • Large-scale image-text pairs used to learn a vision-language space
   • A small set of perfectly labeled boxes
   • Audio recordings
   • Handwritten digit datasets
4. What replaces fixed per-class classifier weights in a common embedding-based open-vocabulary design?
   • The camera's physical image sensor
   • The bounding-box coordinate system
   • All visual features with random file identifiers
   • Representations of the supplied text labels used for similarity-based classification
5. Which of these is an example open-vocabulary or grounding detection model?
   • ResNet-50 alone
   • Grounding DINO
   • A plain k-means clustering script
   • A spreadsheet macro
6. What does an open-vocabulary detector's ability to use a new text category imply?
   • It guarantees accurate detection of every nameable object
   • The category must have been absent from every pretraining image and caption
   • It can attempt localization through learned vision-language associations, with accuracy requiring evaluation
   • It needs no visual features or bounding-box output
7. What output distinguishes text-conditioned object detection from simply saying that an image contains a named category?
   • Only a whole-image tag with no object location
   • Only the text prompt's word count
   • Only the image's dimensions and filename
   • Locations such as bounding boxes for the matching objects
8. Before using open-vocabulary predictions to auto-label a new domain, what should a dataset team check?
   • The boxes and labels under representative prompts, thresholds, and domain conditions
   • Assume text-conditioned predictions are automatically ground truth
   • Verify only that the label strings contain no spelling errors
   • Discard all visual inspection because image-text pretraining guarantees accuracy