Optical Flow Quiz

Answers and explanations appear as you take the quiz.

1. What does optical flow actually estimate?
   • The apparent motion of pixels between consecutive frames
   • The color temperature of each frame
   • The number of objects in a single image
   • The audio waveform accompanying a video
2. What is the 'brightness constancy' assumption used in classic optical flow?
   • Every frame must have the same average brightness
   • A moving point keeps roughly the same brightness across frames
   • Brightness must increase steadily over a video
   • Only black-and-white video can be analyzed
3. Which method uses a 4D correlation volume and recurrent updates for optical flow?
   • Tesseract
   • Mask R-CNN
   • RAFT
   • Swin patch merging
4. Why can classic optical-flow assumptions fail in fast motion?
   • Because frames contain no pixels
   • Because flow is a text-only task
   • Because all motion is uniform
   • Large displacement, occlusion, or brightness changes violate local smoothness or brightness-constancy assumptions
5. Which application relies directly on estimating motion between frames?
   • Video frame interpolation for smooth slow motion
   • Spell-checking a text document
   • Sorting photos by file size
   • Converting speech to text
6. What is an 'occlusion' problem in optical flow?
   • When the video is too brightly lit
   • When a pixel becomes hidden or revealed, so no valid match exists between frames
   • When the frame rate is exactly 30 fps
   • When two videos are merged together
7. What is the output of dense optical flow?
   • One class label for the whole clip
   • A 3D mesh with metric scale
   • A displacement vector estimate for each image pixel
   • A transcript of the audio
8. What is one reason flow can be uncertain in a textureless region?
   • Every pixel is assigned a unique object name
   • The region has too many labels
   • Optical flow only works on text
   • There may be too little local visual information to identify a unique match