Visual Question Answering

Assisting visually impaired users by describing their environment

VQA can answer questions about surroundings, helping visually impaired users navigate and understand their environment independently.

Example questions:

• "What objects are in front of me?"
• "What color is this shirt?"
• "Is there text on this sign, and what does it say?"

Automatically analyzing user-generated images for policy compliance

Instead of pre-defining every prohibited item, ask targeted questions to understand image content and flag potential violations.

Example questions:

• "Does this image contain inappropriate content?"
• "Are there any weapons or dangerous items visible?"
• "Is this image suitable for all audiences?"

Answering product questions directly from product images

Help customers get instant answers about products without reading lengthy descriptions or specifications.

Example questions:

• "What color options are shown?"
• "Is this suitable for outdoor use?"
• "What size does the label indicate?"
• "Does this come with assembly instructions?"

Creating interactive learning experiences

Enable students to explore visual content actively by asking questions about diagrams, historical photos, scientific images, and more.

Example questions:

• "What process is shown in this diagram?"
• "What time period does this photograph represent?"
• "What elements are visible in this chemical reaction?"

Assisting medical professionals with imaging analysis

Help healthcare providers quickly extract information from medical images and patient photos without extensive manual review.

Example questions:

• "Are there any abnormalities visible in this scan?"
• "What is the approximate size of the indicated region?"
• "Does this wound show signs of infection?"

🚧

Important

VQA should assist, not replace, medical professionals. Always have qualified healthcare providers review and validate AI-generated answers.

Enabling robots to understand and respond to questions about their environment

Allow robots to perceive and interpret their surroundings through natural language interaction rather than pre-programmed rules.

Example questions:

• "Is the path ahead clear?"
• "What object is on the table?"
• "How many people are in the room?"
• "Is the door open or closed?"

Good questions are specific and unambiguous:

✅ Good: "What color is the car in the center of the image?" ❌ Too vague: "What do you see?"

✅ Good: "How many people are wearing red shirts?" ❌ Too broad: "Describe everything in the image."

Train your VQA model with varied questions:

• Different question types (yes/no, counting, colors, descriptions)
• Various phrasings for similar questions
• Multiple images showing different scenarios
• Edge cases and challenging examples

More diversity = better generalization to new questions.

When creating training annotations:

• Use consistent answer formats (e.g., always use "yes"/"no", not "yes"/"nope"/"yep")
• Keep answers brief and direct
• Avoid unnecessary elaboration
• Use the same terminology across similar answers

Consistency helps the model learn more effectively.

For good results, aim for:

• Minimum: 100 question-answer pairs across at least 20 images
• Recommended: 500+ pairs across 100+ images

More diverse examples lead to better performance on new questions.

VQA can answer questions about objects (e.g., "Is there a car in the image?"), but it won't draw bounding boxes around them.

For object localization with bounding boxes, use Phrase Grounding instead.

VQA works well for:

• Binary questions: "Is the door open?"
• Counting: "How many people are visible?"
• Attributes: "What color is the car?"
• Descriptions: "What is the person doing?"
• Reasoning: "Is this image suitable for outdoor use?"

Avoid overly complex questions that require external knowledge beyond what's visible in the image.

Yes! You can create multiple datasets with different task types and train separate models, or use them together in your application workflow.

For example: Use Phrase Grounding to detect defects, then use VQA to answer specific questions about each detected defect. Learn more about phrase grounding.