Question 1

True or false: Facial recognition systems are always unbiased and equally accurate across all demographics.

Accepted Answer

False. Many systems show bias, with higher error rates for women and people of color due to imbalanced training data.

Question 2

True or false: Computer vision models can only process static images, not videos.

Accepted Answer

False. Video processing uses techniques like optical flow and 3D convolutions to analyze temporal sequences frame by frame.

Question 3

True or false: Convolutional neural networks can detect objects even if they are partially hidden behind other objects.

Accepted Answer

True. CNNs learn hierarchical features, allowing them to recognize objects from visible parts, a capability called occlusion handling.

Question 4

True or false: The first computer vision system was built in the 1960s to recognize handwritten digits.

Accepted Answer

False. Early work in the 1960s focused on simple shape recognition, like identifying blocks and cylinders, not handwritten digits.

Question 5

True or false: A computer vision algorithm can estimate the 3D shape of an object from a single 2D photograph.

Accepted Answer

True. Techniques like shape-from-shading and deep learning models can infer depth and 3D structure from just one image.

Question 6

True or false: Some computer vision models can detect sarcasm or emotional tone from facial expressions alone.

Accepted Answer

True. Advanced emotion recognition models analyze micro-expressions and context, though detecting sarcasm is still highly challenging and limited.

Question 7

True or false: All computer vision systems require labeled training data to function correctly.

Accepted Answer

False. Self-supervised and unsupervised methods, like contrastive learning, can learn visual representations without any labels.

Question 8

True or false: Computer vision models can sometimes be fooled by a single pixel change in an image.

Accepted Answer

True. Adversarial attacks show that tiny, imperceptible pixel alterations can cause models to misclassify objects, even with high accuracy otherwise.

Computer Vision Trivia Questions