AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is 'zero-shot object detection' in computer vision?
⚠ Common exam trap
Many candidates confuse the term 'zero-shot' with performance metrics like latency, image color depth, or accuracy, rather than understanding it as a training paradigm where the model generalizes to unseen classes via natural language descriptions.
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
✓
Detecting objects described in text without any training examples of that specific class
Zero-shot object detection refers to a model's ability to detect objects in images based on a textual description of the target class, without having been trained on any labeled examples of that specific class. This is achieved by leveraging a joint embedding space where visual features and text features are aligned, allowing the model to generalize to unseen categories at inference time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Object detection that runs with zero latency for real-time applications
Why it's wrong here
This option describes an engineering optimization target, not a learning paradigm. Latency depends on hardware, model architecture, quantization, and pruning, and it measures when results arrive, not what the model can understand. In zero-shot detection, the model computes similarity between image features and text embeddings to handle novel classes, a capability entirely independent of inference speed.
- ✓
Detecting objects described in text without any training examples of that specific class
Why this is correct
Zero-shot object detection uses a vision-language embedding space in which image regions and text descriptions are projected into the same representational space. At inference, the model scores each region against the embedding of the provided text prompt and outputs bounding boxes for regions with high alignment. Because the target class is defined semantically by text rather than by labelled training images, the detector can find object categories that were never seen during training.
- ✗
Detection that works on black and white images (zero colour channels)
Why it's wrong here
Grayscale input is an image-preprocessing or network-channel consideration; object detectors can be adapted to accept single-channel inputs without changing what 'zero-shot' means. The term zero-shot refers to zero labelled examples of the target class during training, not zero color channels. These are unrelated uses of 'zero', so this is not a valid definition of zero-shot object detection.
- ✗
An object detection model with zero false positives on the test set
Why it's wrong here
This statement confuses an evaluation metric with a capability definition. Achieving zero false positives on a specific test set means the model had perfect precision on those particular images, but it says nothing about generalizing to unseen categories. Zero-shot object detection refers specifically to the ability to localize objects described in a text prompt without having been trained on labelled examples of that class.
Go deeper
Related to this question
Learn chapter
What is Computer Vision?
Key term
Embedding
Embedding is the process of converting high-dimensional data like text or images into a lower-dimensional numerical vector that captures semantic meaning for use in machine learning models.
Key term
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
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