Vhura-Vocabulary Object Detection Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Chii chinotsanangudza kugona kwekuvhura-mazwi ekuona chinhu?
   • Kutsvaga chete zvikamu zvinoonekwa nemabhokisi panguva yekudzidziswa
   • Kumhanya nekukurumidza kupfuura mamwe ese ma detectors
   • Kucherekedza zvinhu zvinotsanangurwa nemavara akasarudzika, kusanganisira zvikamu zvisingaonekwe
   • Kushanda chete pa grayscale mifananidzo
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. Ndeipi pretraining sosi inonyanya kugonesa kuvhurika-mazwi ekuziva?
   • Zvikuru-zvikuru zvemifananidzo-zvinyorwa zvakashandiswa kudzidza nzvimbo yemutauro wechiratidzo
   • Chidimbu chidiki chemabhokisi akanyorwa zvakakwana
   • Zvakarekodhwa
   • Maseti edijiti akanyorwa nemaoko
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. Ndeupi weiyi muenzaniso wakazaruka-mazwi kana modhi yekuona yepasi?
   • ResNet-50 chete
   • Grounding DINO
   • A plain k-zvinoreva 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